Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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\n
1. Introduction
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Neuroimaging has meant a major breakthrough for the diagnosis and treatment of neurodegenerative diseases. Not so long ago, biomedical signal processing was limited to filtering, modelling or spectral analysis, prior to visual inspection. In the past decades, a number of powerful mathematical and statistical tools have been developed and evolved together with an increasing development and use of neuroimaging. Structural modalities such as computed tomography (CT) or the widely known magnetic resonance imaging (MRI), and later functional imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) provide unprecedented insight in the internals of the brain, allowing the study of the structural and functional changes that can be linked to neurodegenerative diseases. This means a huge amount of data where automatic tools can help to identify patterns, reduce noise and enhance our knowledge of the brain functioning.
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Computer-aided diagnosis (CAD) systems in neuroimaging include a variety of methods that range from preprocessing of the images (just after acquisition) to advanced machine-learning algorithms to identify disease-related patterns. Algorithms used in the reconstruction of medical imaging, such as the tomographic reconstruction (TR) or the filtered back-projection (FBP) lay outside the scope of this chapter, focused on the application of CAD systems to neuroimaging.
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This chapter starts with an exposition of the preprocessing methods used in different neuroimaging modalities, including registration, normalization and segmentation. We provide references on the algorithms behind well-known pieces of software such as statistical parametric mapping (SPM) [1], FreeSurfer [2] or the FMRIB Software Library (FSL) [3]. Later, the most used computer-aided diagnosis systems in psychiatry, psychology and neurology are described. These include SPM [1] and voxel-based morphometry (VBM) [4], voxels as features (VAFs) [5] and how the computation of regions of interest (ROIs) work in semiquantitative analysis. In the next section, new advances in neuroimaging analysis are presented, starting with the basis of machine learning and classification, including support vector machines (SVMs) [5–8], but also logistic regression [9, 10] or classifier ensembles [11, 12]. Given the characteristics of neuroimaging data, where we study large, possibly correlated, data, the extraction of higher-level features is essential. Therefore, in the last section, we provide an introduction to commonly used image decomposition algorithms such as principal component analysis (PCA) [8, 13–18] and independent component analysis (ICA) [19–22]. Finally, other recent feature extraction algorithms including spatial and statistical methods such as texture analysis [23–31], morphological tools [31–33] or artificial neural networks [34–40] are presented.
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2. Preprocessing
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Preprocessing of the neurological images is a fundamental step in CAD systems as it ensures that all the images, either structural or functional, are comparable. We consider a preprocessing step, an algorithm that, applied after the acquisition and reconstruction of the images–usually a machine-dependent procedure–is intended to produce directly comparable images that represent a certain magnitude. The number and type of procedures to follow in preprocessing differs from one modality to another, although normalization and smoothing are used throughout all of them (see Figure 1).
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2.1. Spatial normalization or registration
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The anatomy of every subject’s brain is slightly different in shape and size. In order to compare images of different subjects, we need to eliminate these particularities and transform the images so that the subsequent group analysis or comparison can be performed. To do so, the individual images are mapped from their individual subject space (current anatomy) to a reference space, a common anatomical reference that allows the comparison. This procedure is known as spatial normalization or registration.
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There are a number of algorithms used in image registration, but the procedure usually involves the computation of a series of parameters to map the source images to a template that works as a common anatomical reference (see Section 2.1.2 for an overview on registration algorithms). The most widely used template is the Montreal Neurological Institute (MNI) template.
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Figure 1.
Example of the pipeline followed in structural MRI preprocessing, comprising spatial normalization, smoothing and segmentation.
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2.1.1. The MNI space and template
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The MNI space is the most widely used space for brain registration and was recently adopted by the International Consortium for Brain Mapping (ICBM) as its standard template. It defines a standard three-dimensional (3D) coordinate system (also known as ‘atlas’), which is used to map the location of brain structures independently of the size and shape of each subject’s brain.
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The MNI space was intended to replace the Talairach space, a system based on a dissected and photographed brain for the Talairach and Tournoux atlas. In contrast to this, the MNI created a new template that was approximately matched to the Talairach brain but using a set of normal MRI scans. The current standard MNI template is the ICBM152 [41], which is the average of 152 normal MRI scans that have been matched to an older version of the MNI template using a nine-parameter affine transform.
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2.1.2. Registration algorithms
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Algorithms used in registration can be categorized in linear transformations (being the affine transform the most complex) and non-rigid or elastic transformations. Affine transformations are applied as a matrix multiplication and include terms for translation, scale, rotation, shear, squeeze and so on. These lineal transformations are applied globally to the image and therefore do not account for local geometric differences. Most neuroimaging software includes some kind of affine registration, including FreeSurfer [2], FSL (via package FLIRT) [3] or Elastix.
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The estimation of the parameters is performed via the optimization of a given cost function, the minimum-squared difference between the source image and the template being the most basic. Modern software include more refined functions, for example, Tukey’s biweight function (in mri_robust_template of FreeSurfer) [11], or the mutual information (in FLIRT) [42], that operate under a high-complexity schema involving local and global multiresolution optimization. When working with images of the same modality, the preferred cost function is the minimum-squared difference between the source image and the template, whereas in the case of multimodal registration, the maximization of the mutual information is preferred.
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Non-rigid transformations can apply local transformation to align the target image to the template. Many of these non-rigid transformations are applied as a local fine-tuning after a previous affine transformation, although some of them use higher-complexity models that do not need this previous step. Some non-rigid transformations include radial basis functions (RBFs) (thin plate or surface splines, multiquadrics and compactly supported transformations), physical continuum models and large deformation models (diffeomorphisms). Of these, the most popular are diffeomorphic transformations, which feature the estimation and application of a warp field, and they are used in SPM (default) [1], FreeSurfer [2], FSL (FNIRT) [3], Elastix or ANTs.
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2.1.3. Co-registration
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Sometimes, we use different modalities, usually a functional and a structural image, for the same subject. It would be therefore very useful that these two (or even more) different images spatially match each other, so that any processing can be fit to any of them and applied to all. Since functional images have low resolution, the procedure for performing spatial normalization frequently involves co-registration to the structural image, which has a higher level of detail. Then the spatial normalization (or registration) parameters are estimated on the structural image and applied to all the co-registered maps. In the context of low-resolution functional imaging, affine co-registration to the structural image is preferred.
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2.2. Smoothing
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Despite the spatial normalization applied to the images, differences between subjects are still a problem that can reduce the signal-to-noise ratio (SNR) of the neuroimaging data. This problem increases as the number of subjects increases. To increase the SNR, it is recommended to filter out the highest frequencies, that is, applying a smoothing. Smoothing removes the smallest scale changes between voxels, making the detection of large-scale changes easier.
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On the other hand, smoothing the images lowers its resolution. Smoothing is usually applied using a 3D Gaussian kernel to the image, determined by its full-width at half maximum (FWHM) parameter, which is the diameter of the smoothing kernel at half of its height. The choice of the size of the kernel is therefore of fundamental importance, and it depends on the signal to be detected. A small kernel will make our further processing confound noise with activation signal, but a larger kernel can parse out significant signal. As a general rule, the size of the kernel should be smaller than the activation to be detected.
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2.3. Functional MRI-specific steps
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The acquisition of functional MRI (fMRI) involves a more complex preprocessing of the images, given its dynamic features. fMRI studies acquire a long sequence of low-resolution MRI images that contain a magnitude known as blood-oxygen-level-dependent (BOLD) contrast. This sequence of three-dimensional volumes is combined into a four-dimensional (4D) volume that conceptually works similar to a video. In this context, the outcome can be much more affected by the subject motion. Therefore, procedures such as slice-timing correction and motion correction are mandatory in fMRI.
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2.3.1. Slice-timing correction
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In fMRI, the scanner acquires each slice within a single brain volume at different times. Different methods are used to acquire the slices: descending order (top to down), ascending order (bottom to up) and interleaved (the slices are acquired in a certain sequence). The time interval between one slice and another is usually small, but after acquiring a whole brain volume, there might be a difference of several seconds between the first and the last acquisition.
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To compensate for the time differences between slice acquisition within a single volume, a slice-timing correction is performed by temporally interpolating the slices so that the resulting volume is approximately equivalent to acquiring the whole brain at the same time point. These time differences are especially important in event-driven experimental design, where timing is very relevant. Linear, quadratic or spline functions are used to interpolate the slices.
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2.3.2. Motion correction
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Motion correction, also known as realignment, accounts for the undesirable effect of head movement during the acquisition of the data. It is almost impossible for a subject to lie perfectly still during the acquisition of an fMRI sequence, and even the small movements can lead to variation in the BOLD values that, if uncorrected, can lead to activation of false positives.
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The correction of this problem usually uses a rigid-body transformation similar to those used in registration. In this case, a model characterized by six parameters that account for translation and rotation is frequently used. The parameter estimation is performed by minimizing a cost function, such as correlation or mutual information, between the volumes and a reference time volume, usually the mean image of all time points.
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Sometimes, the movement of the head is so fast that motion correction cannot correct its effects. In that case, the most used approach is to eliminate the images acquired during that fast movement, using an artefact detection algorithm that identifies large variations between images at adjacent time points.
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2.4. Intensity normalization
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Most functional neuroimaging modalities, in contrast to unitless structural MRI images, are the representation of the distribution of a certain contrast over the brain. There exist a larger number of sources of variability that can affect the final values: contrast uptake, radiotracer decay time, metabolism, and so on. In order to establish comparisons between subjects, an intensity normalization procedure is required.
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Intensity normalization methods are to be linear in nature, since it is essential to maintain the intensity ratio between brain regions, acting on the whole brain. In its simplest form, it consists of a division by a constant. This parameter is often estimated [6, 7] as the average value of the 95th bin of the histogram of the image, that is, the average of the 5% higher-intensity values, in what is known as the normalization to the maximum. Another approach, called integral normalization, estimates this parameter as the sum of all values of the image. A more complex approach requires a priori knowledge of the distribution of intensity in a normal subject. This is designed so that the whole image is divided by the binding potential (BP) [43], a specific-to-non-specific ratio between the intensities in areas where the tracer should be concentred and the non-specific areas.
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Then, we have general linear transformations, defined as Y = aX + b. These procedures use estimates of the probability density function (PDF) of the source images and then estimate the parameters a and b, which transform their original PDF to an expected range. Methods to estimate the PDF parameters range from the simplest, non-parametric histogram [44] to more advanced estimates such as an analysis of covariance (ANCOVA) approach used in SPM [44, 45], or parametric estimates involving the Gaussian or the more general alpha-stable distribution [46], which has been recently tested with great success.
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Structural modalities also suffer from some sources of intensity variability, for example, magnetic field inhomogeneity, noise, evolution of the scanners, and so on. Field inhomogeneity causes distortions in both geometry and intensity of the MR images [47], usually addressed via increasing the strength of the gradient magnetic field or preprocessing. Intensity variability is especially noticeable in multicentre-imaging studies, where images should share certain characteristics. To improve the homogeneity of a set of structural images acquired at different locations, the use of quantitative MRI images has been recently proposed [48]. In contrast to typical unitless T1-weighted images, quantitative imaging can provide neuroimaging biomarkers for myelination, water and iron levels that are absolute measures comparable across imaging sites and time points.
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2.5. Segmentation
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Segmentation, mostly of structural MRI images, involves a series of algorithms aimed at constructing maps of the distribution of different tissues. The general approach is to separate the image in three different maps containing grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF), although some software can also output data for bone, soft tissue or very detailed functional regions and subregions [49–51]. The procedure is applied after all aforementioned steps, including field inhomogeneity correction, which is essential for a correct segmentation. Here, we provide insight on the most used algorithms for segmentation
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2.5.1. Statistical parametric mapping
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In SPM, the segmentation procedure uses an expectation-maximization (EM) algorithm to obtain the parameters corresponding to a mixture of Gaussians that represents the tissue classes. Afterwards, an affine transformation is applied using tissue probability maps that are in the ICBM/MNI space. It currently takes normalized MRI images and extracts up to six maps: GM, WM, CSF, bone, soft tissue and air/background.
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2.5.2. FMRIB software library
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Two algorithms are used in FSL to perform segmentation: the FMRIB Automated Segmentation Tool (FAST) and the FMRIB Integrated Registration and Segmentation Tool (FIRST).
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FAST is based on a hidden Markov random field model optimized through the EM algorithm. It firstly registers the brain volume to the MNI space and then segments the volume into three tissue classes (GM, WM and CSF). Skull-stripped versions of the anatomical image are needed.
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On the other hand, FIRST is intended to extract subcortical structures of the brain characterized by parameters of surface meshes and point distribution models located in a database, built using manually segmented data. The source images are matched to the database, and the most probable structure is extracted based on the shape of the image.
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2.5.3. FreeSurfer
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FreeSurfer uses surfaces to perform posterior analysis, such as cortical thickness estimation. Therefore, the main aim of the command reckon_all, which performs most preprocessing steps, is not to obtain new image maps containing tissues, but surfaces that identify the different areas in the brain.
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After registration to the MNI305 space, voxels are classified into white mater and other based on their location, intensity and local neighbourhood intensities. Afterwards, an estimation of the bias field is performed using these selected voxels, to correct the image. Next, the skull is stripped using a deformable template model [49]. The hemispheres are separated using cutting planes based on the expected MNI305 location of the corpus callosum and pons, and several algorithms that detect these shapes in the source images. The surfaces for each hemisphere are estimated first using the outside of the white matter mass, and then refined to follow the intensity gradients between WM and GM, and GM and CSF, called the pial surface, which will allow the estimation of cortical thickness [50].
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FreeSurfer also implements a volume-based stream designed to obtain cortical and subcortical tissue volumes and label them. This stream performs a different, pathology-insensitive affine registration to the MNI305 space, followed by an initial volumetric labelling. Then, the intensity bias is corrected, and a new non-linear alignment to a MNI305 atlas is performed. The labelling process is a very complex one and is more thoroughly explained in Ref. [51].
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3. Basic analyses
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After performing a preprocessing of the source images, many procedures can be applied to extract the information required for clinical practice. In this section, we focus on those analyses that are more extended in clinical practice. These are currently preferred by medical staff, since they are easily interpretable and require little knowledge of computer science. Nevertheless, they are computer-aided systems that yield significant information to assist in the procedure of diagnosis. In later sections, we develop the application of more advanced systems that make use of machine learning to help in the same procedure.
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3.1. Analysis of regions of interest (ROIs)
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The analysis of regions of interest (ROIs) is the most basic computationally aided analysis of neuroimaging. It involves either purely manual or computer-assisted delimitation of regions in both structural and functional imaging and a posterior analysis of the delimited volumes.
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A number of analyses can be performed on these regions, depending on the image modality. In the case of structural MRI, a frequent approach is the estimation of the volume of cortical and subcortical structures, called morphometry. The delimitation of ROIs is often performed automatically and then manually refined and allows the quantification of diseases that alter the normal distribution and size of GM and WM. This is the case of brain atrophy, a common issue in dementia, or brain tumours.
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This ROI analysis is hardly used in fMRI, but very extended in nuclear imaging (PET or SPECT). Since the maps obtained with these techniques quantify the uptake of certain drugs, the total uptake can be obtained as the sum of intensities inside the drawn volume. However, the most used measure in these modalities is a ratio between the intensities in specific and non-specific areas, especially with drugs that bind to specific targets such as dopamine, amyloid plaques, and so on.
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3.1.1. Cortical thickness
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A specific case of a fully computer-assisted ROI analysis is the cortical thickness measure performed in FreeSurfer [50]. As aforementioned, once the GM-WM and GM-CSF surfaces have been estimated, the amount of GM in a direction perpendicular to the surface can be estimated. Combined with the subcortical segmentation algorithms, this allows to a very powerful estimation of the average cortical thickness by anatomical region. Thus, per region GM differences such as atrophy or hypertrophy can be characterized, making it perhaps the most widely used method in neuroscience.
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3.2. Voxel-wise analyses
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To overcome the time-consuming procedure of the traditional analysis of ROIs, several algorithms that act at the voxel level have been proposed. These include the statistical parametric mapping (SPM), a voxel-based morphometry (VBM) or the first machine-learning approach in this chapter, called voxel as features (VAF) (see Figure 2).
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Figure 2.
Example of a voxel-based morphometry output. Significant areas (p<0.05,| t |>2.56) in a comparison of autism-affected patients and healthy controls are highlighted.
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3.2.1. Statistical parametric mapping (SPM)
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Statistical parametric mapping (SPM) was proposed by Friston et al. [45] to automatically examine differences in brain activity during functional-imaging studies involving fMRI or PET. The technique can be applied to both single images (PET) and time series (fMRI), and the idea behind it is construct a general linear model (GLM) to describe the variability in the data in terms of experimental and confounding effects.
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The level of activation is assessed at a voxel level using univariate statistics, and featuring a correction for type I errors (false positives), due to the problem of multiple comparisons. In the case of time series analysis, a linear convolution of the voxel signal with an estimation of the hemodynamic response is performed, and then the activation is tested against the analysed task.
\n
The representation of the activation is frequently presented as an overlay of the Z-scores obtained for each voxel after the multiple comparisons correction on a structural image. The Z-score–or standard score–is the signed number of standard deviations an observation is above the mean. The resulting maps allow a visual inspection of the active brain areas, which can later be related to a certain disease or task.
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3.2.2. Voxel-based morphometry (VBM)
\n
Voxel-based morphometry (VBM) is the application of SPM to structural MRI images [4]. The principle behind VBM is the registration to a template, and then a smoothing of the structural image so that the smaller anatomical differences between subjects are reduced. Finally, a GLM is applied voxel-wise to all the images, in order to obtain a Z-score map that highlights the areas where the differences are greater.
\n
As commented in Section 2.2, the size of the smoothing kernel is an important parameter. A small kernel will lead to artefacts in the Z-maps, including misalignment of brain structures, differences in folding patterns or misclassification of tissue types. On the other hand, a larger kernel will not be able to detect smaller regions.
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Newer algorithms expand the idea behind VBM using multivariate approaches, to reveal different patterns. These algorithms include an independent component analysis (ICA) decomposition of the dataset and conversion to Z-scores, called source-based morphometry [52] and a multidimensional tensor-based morphometry [53].
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3.2.3. Voxels as features (VAF)
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Voxel-as-features (VAFs) is another voxel-wise approach proposed in Ref. [5] for the diagnosis of Alzheimer’s disease using functional imaging. It can be considered the first machine-learning approach in this chapter, since it features a linear support vector machine (SVM) classifier whose input features are the intensities of all voxels in the images. It has been used in many works [6, 13, 25, 32, 33] as a baseline and an estimation of the performance achieved by expert physicians by means of visual analysis.
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Additionally, some improvements can be done over the raw VAF, for example, using statistical hypothesis testing to obtain the most significant voxels, thus reducing the computational load and increasing the accuracy. The weight vector of the linear SVM can be inversely transformed to the dimension of the original images, and therefore provide a visual map that reflects the most influential voxels, in a similar way to the Z-maps of SPM and VBM.
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4. Advances in brain image analysis
\n
The application of new machine-learning techniques in CAD systems is a current trend. Works on this topic have increased exponentially in the past 10 years, and it is expected to grow even more. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data, and therefore it is very useful in neuroimaging.
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Two approaches exist in machine learning. Supervised learning explores the patterns that lead to a certain outcome, for example, the brain activation patterns that are related to a certain disease. On the other hand, unsupervised learning explores the underlying structure of the data. Machine learning in CAD is mostly based on supervised learning, since it is focused on the prediction and analysis of patterns related to a certain disease.
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In its simplest form, a machine-learning pipeline for neuroimaging consists of a single classifier, just as the VAF approach that we mentioned. However, classifiers can improve their detection power if higher-level features are extracted from the data, for example, features that represent the distribution of the voxel intensities, the texture of the images or the sources of variance of the maps. This is known as feature extraction, and the most common technique is image decomposition.
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4.1. Classification in neuroimaging
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We mentioned that the simplest approach to machine learning is classification. Classification basically is induction, that is, using a set of samples extracted from the real world from which we know their class (also known as label or category), and build a model that can identify the class of new samples that were never seen.
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Statistical classification is a fertile field, and many classification algorithms are being developed at the moment. A wide range of strategies exist, among them are the following: instance-based methods, where the new samples are classified by a measure of similarity with known samples; hyperplane-based methods, where a multidimensional function that weights the input features produces a score that identifies the class; decision trees, where the information is encoded in hierarchical nodes that test one feature at a time; classifier ensembles, artificial neural networks and many others.
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Hyperplane-based methods are perhaps the most extended in neuroimaging and among them, by far, support vector machines (SVM) [6–8]. They offer high accuracy in high-dimensional spaces and can be expanded with the use of kernels to tackle non-linearly separable data. Furthermore, they are very robust to overfitting (unlike decision trees) and therefore more generalizable. Other frequent methods include the hyperplane-based logistic regression [9, 10]—and its multiclass version, the Softmax classifier–decision trees and random forests [54–56], and ensembles of neural networks [34] or ensembles of SVM [11, 12].
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4.1.1. Support vector machines (SVM)
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Support vector machines (SVM) have been widely used in neuroimaging [5–8] and other high-dimensional problems due to their overfitting robustness. In its linear version, training the classifier is equivalent to finding the coefficient vector that defines the separation hyperplane:
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(w→xi→−b)=0E1
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so that the distance between the hyperplane and the nearest point is maximized. That way, the problem reduces to minimize the loss function (based on the Hinge loss):
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[1n∑i=1nmax[0,yi(w→x→i−b)]]+λ‖w→‖2E2
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where yi is the class of the i-th training sample, b is the bias so that b/ is the offset of the hyperplane from the origin in the direction of , and λ is the regularization term, used to keep the coefficients in as small as possible. Many methods exist to perform this optimization, for example, gradient descent or primal and dual loss (using quadratic programming).
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Despite the fact that SVMs are mostly used in its linear form, an extension to non-linearly separable problems can be made using the kernel trick. By using kernels, we can implicitly transform data to a higher-dimensional space, but without needing to work directly in that transformed space, simply replacing all dot products in the SVM computation with the kernel. A kernel is a function K:ℝN×ℝN→ℝ so that , where 〈·,·〉M is the dot product in ℝM and is a function that transform x→i to that higher-dimensional space. Some common kernel functions are the polynomial kernel or the radial basis function (RBF) .
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4.1.2. Ensembles of classifiers
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Ensembles of classifiers are a recent approach to machine learning in which, instead of choosing the best classification algorithm, we train and test different classifiers with different properties, and then combine their outputs to obtain the prediction. In the simplest technique, called bagging, we combined the results of the classifiers by voting. This is the strategy used in most neuroimaging works, for example in [11] where the brain was divided into subvolumes and a classifier was assigned to each one. In boosting weights are assigned to training samples, and then varied so that new classifiers focus on the samples where previous classifiers failed. Finally, in stacking, the outputs of individual classifiers are used as features in a new classifier that combines them. This was the approach used in spatial component analysis (SCA) [12], where an ensemble of M SVM was trained on M anatomically defined regions, and their outputs were combined to define a new decision function based on Bayesian networks.
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4.2. Image decomposition
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Signal decomposition algorithms are the first feature extraction algorithms that we will deal with. They are aimed at modelling a set of samples as a linear combination of latent variables. These latent variables or components can be thought of as the basis of an n-dimensional space where each sample is projected and represented as an n-dimensional vector. In a general case applied to our neuroimaging data, the source images X can be modelled as
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X=s0w→0+s1w→1+⋯+sNw→N+ϵ=s→W+ϵE3
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where si is the coordinate (or component score) of the current image in the ith dimension of the new space defined by all the base vectors (component loadings), and ϵ is the error of the estimation. Figure 3 shows an illustration of the process.
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Signal decomposition techniques are widely used in many applications, ranging from one-dimensional signals such as audio or electroencephalography (EEG) to multidimensional arrays, and are frequently applied as feature reduction to overcome the small sample-size problem, that is, the loss of statistical power due to a larger number of features compared to the number of samples.
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4.2.1. Principal component analysis (PCA)
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Principal component analysis (PCA) is a decomposition method where each component (here known as principal components) is intended to model a portion of the variance. The order of the components is set so that the greatest variance in the component space comes to lie on the first coordinate, the second greatest variance on the second coordinate, and so on. The set of principal component loadings, W, is the set of eigenvectors obtained when applying the eigenvalue decomposition of the covariance matrix of the signal, that is, XTX. It is not uncommon for neuroimaging specialist to refer to these eigenvectors as eigenbrains.
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Figure 3.
Illustration of a decomposition procedure of a PET-FDG brain image using PCA.
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The most frequent approach to compute PCA on a given dataset is by using the singular value decomposition (SVD). This performs the decomposition of X in eigenvalues and eigenvectors in the form
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X=UΣWTE4
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Here, U and W are square matrices, respectively, containing n and p orthogonal unit vectors of length n and p, known as left and right singular vectors of X; and Σis an n-by-p rectangular diagonal matrix that contains the singular values of X. When compared to the eigenvector factorization of XTX, it is patent that the right singular vectors X of are equivalent to the eigenvectors of XTX, while Σ, the singular values of X, are equal to the square roots of the eigenvalues of XTX, therefore, the set of principal component scores S of X in the principal component space can be performed as
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S=XWE5
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As can be seen here, PCA does not account for the noise independently, but it integrates it in the model as another source of variance. The component-loading matrix can be truncated (i.e., only the first m components are used) to reduce the dimension of the principal component space, which is its most used application.
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PCA has been used in many neuroimaging works, mainly used for feature reduction in a classification pipeline of nuclear imaging [13–15], but also in structural MRI [16, 17], functional MRI [18] or EEG signals [8].
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4.2.2. Independent component analysis (ICA)
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Independent component analysis (ICA) performs a decomposition of the source images, but, unlike PCA, it assumes that the components are non-Gaussian and statistically independent from each other. Independence of the components is assessed via either the minimization of the mutual information or checking the non-Gaussianity of the components (motivated by the central limit theorem).
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There exist a number of algorithms that implement ICA, from which the most extended are FastICA [57] and InfoMax [58], although other alternatives, such as CuBICA, JADE or TDSEP, are available. Most algorithms use an initial data preprocessing involving centring (create a zero mean signal by subtracting the mean), whitening and dimensionality reduction (with eigenvalue decomposition, SVD or PCA). Afterwards, the algorithm performs a decomposition of each of the m components by minimizing the cost function based on the independence criteria, usually the InfoMax, the Kullback-Leibler divergence or maximum entropy in the case of mutual information algorithms and kurtosis or negentropy for the non-Gaussianity-based algorithms. Most ICA algorithms require the number of sources m as an input.
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The terms used in ICA vary from the ones used in PCA decomposition, although they roughly represent the same concepts. It is all based on a mixing model, where the original data X, known as mixed signal, is considered a mixture of several unmixed sources S, and therefore the matrix W that projects X to the ICA space S is known as the unmixing matrix.
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A basic version of the FastICA algorithm is intended to find a unit vector for the pth component so that the projection maximizes non-Gaussianity, measured using an approximation of negentropy . To do so, first, we initialize the weight vector to random. Then, using the derivative of the negentropy estimates, we perform the update:
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w→p+=E{x→g(w→px→)}−E{g(w→px→)}w→pE6
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w→p=w→p+/||w→p+||E7
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This is performed for all components and iterated until convergence. The outputs must be decorrelated after each iteration to prevent different vectors from converging to the same maxima. This is achieved, after pooling all to the unmixing matrix w in a matricial form, using the alternative found in [57]
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W=W/||WWT||E8
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and repeating
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W=32W−12WWTWE9
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until convergence.
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Many neuroimaging works have also applied ICA for feature extraction and reduction in classification pipelines [19, 20], but its main application today is in the processing of EEG signals [21, 22].
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4.3. Other feature extraction techniques
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In this section, we address feature extraction techniques other than the aforementioned decomposition algorithms. The philosophy behind them is still to provide higher-level features that allow a feature space reduction to overcome the small sample-size problem, but they are intended to extract and quantify information that otherwise would not be available.
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4.3.1. Texture analysis
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Texture analysis is any procedure intended to classify and quantify the spatial variation of voxel intensity throughout the image. In neuroimaging, they are more commonly used to classify images or to segment them (which can be also considered a form of classification). Depending on the number of variable studies, we can divide the methodology into first-, second- and higher-order statistical texture analysis. In first-order statistics, only voxel intensity values are considered, and values such as average, variance, kurtosis or frequency (histogram) of intensity values are computed.
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Second-order statistical texture analysis is by far the most popular form. It is based on the probability of finding a pair of grey level at a certain distance and orientation on a certain image. Most algorithms are based in the grey level co-occurrence matrix (GLCM), in which is known as Haralick texture analysis [59]. The GLCM can be thought of as a representation of the frequency of the adjacent pixel variations in a certain spatial direction and distance. For a three-dimensional n×m×k brain image I and two different grey levels i and j, at a given offset Δ the co-occurrence value is defined as
where p = (x, y, z) is the spatial position where x = 1 … n, y = 1, … m, z = 1, … k, and Δ=(dx,dy,dz) is the offset vector, which accounts for the direction and distance between voxels. The size of the GLCM matrix CΔ whose elements were defined before depends on the number of grey levels used in the image posterization, usually 8 or 16. Twelve Haralick texture features were proposed in [59] using a normalized co-occurrence matrix however, many more have been developed throughout the years. We provide formulas for energy entropy or contrast among others. A 3D co-occurrence matrix was used for the analysis of MRI [23], segmentation [24] and analysis of SPECT images [25].
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Higher-order analyses include the grey-level run length method (GLRLM) [26] that computes the number of grey-level runs of various run lengths. Each grey-level run is a set of consecutive and collinear voxels having the same grey-level value. Other texture-based feature extraction methods that have been applied in neuroimaging are the wavelet transform [27], the Fourier transform, used for segmentation [28] and characterization of MRI images [29], or local binary patterns (LBPs) [30, 31].
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4.3.2. Spherical brain mapping (SBM)
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The spherical brain mapping (SBM) is a framework that performs feature reduction of neuroimaging from three-dimensional volumes to two-dimensional maps representing a certain feature [32]. This is done by establishing a spherical coordinate system centred at the anterior commissure (AC) and defining a mapping vector at an elevation (θ) and azimuth (φ) angles. The sets of voxels Vθ,φ crossed by this vector are used to compute a value such as the average, variance or entropy, in each coordinate, and finally these values are used as pixels in a two-dimensional map representing that amount. This converts million-voxels volumes to several-thousand images, which achieves a significant computational load reduction as well. Maps for average, entropy or kurtosis of an MRI image are shown in Figure 4.
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Figure 4.
Example of the average, entropy and kurtosis maps generated by SBM.
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An extension using volumetric LBP was recently proposed [31], and another utility that uses hidden Markov models to compute the path in the direction (θ,φ) using similar-intensity measures was later proposed [33].
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4.3.3. Artificial neural networks (ANN)
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Artificial neural networks (ANN) were inspired by the analysis of biological neural networks in the second half of the twentieth century, but then diverged from their original goal and now are a diverse area of research in machine learning. ANNs are composed by neurons, which model the behaviour of biological neurons by adding up the input signals xi, using different weightings wi and producing an output by means of an activation function f(x). Over the time, activation functions such as sigmoid or tanh have been used, although nowadays the most common is rectified linear unit (ReLU), that computes the function f(x)=max(0,x) (see Figure 5).
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The architecture of an ANN is a series of interconnected layers containing neurons. The perceptron, one of the most basic approaches, comprises an input layer (no neurons), a hidden layer (composed by n neurons connected with all inputs and all neurons in the output layers) and one output layer. ANNs have been proven to be universal approximators. This means that given any continuous function f(x), and some error ϵ>0, there exist an ANN g(x) with at least one hidden layer such that |f(x)−g(x)|<ϵ. The addition of more layers (a deeper network) allows the ANN to gain more abstract knowledge in the training process, especially in convolutional networks, where different types of layers coexist. In these networks, the deeper the ANN is, the more abstract the internal representation of the data will be.
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Figure 5.
Illustration of the architecture of a perceptron with two hidden layers.
\n
The field is extremely vast and there exist countless architectures, but the most common when applying them to neuroimaging are the multilayer perceptron [34] and self-organizing maps (SOMs) for segmentation [35] and pattern recognition in functional imaging [36], and more recently convolutional networks, which are the base of deep learning, used in segmentation [37], feature extraction [39] and classification [38, 40].
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5. Conclusions
\n
Computer-aided diagnosis systems are currently a thriving area of research. The bases are already established and contained in widely used software such as SPM or FreeSurfer. The neuroimaging community already uses these in their daily work, both at research and at clinical practice, with great benefit for the patients. These pieces of software usually include preprocessing (registration, intensity normalization, segmentation, etc.) and posterior automated procedures, such as ROI analysis, VBM or SPM, just like we saw in Sections 2 and 3.
\n
In addition to this, state-of-the-art CAD systems involve the use of advanced techniques to characterize neuroimaging data. The field is still being developed and relevant breakthroughs are still to be made. Advances are being made in a daily basis, with the development of new image modalities involving highly specific radiotracers, advanced registration, correction of inhomogeneities or application of existing machine learning and large data algorithms.
\n
In this review, we have revealed a tendency towards fully automated tools capable of processing neuroimaging data, extract information and even predict the likelihood of having a specific condition. It is very likely that neuroimaging techniques will continue to increase its resolution and usage, and in this scenario the amount of data available will grow exponentially. CAD systems involving most of the topics that we covered in this chapter will be therefore crucial in clinical practice to provide understanding of all available information, otherwise intractable. Only this way can we address a major challenge: to discover meaningful patterns related to behaviour or diseases that ultimately help us to understand how the brain works.
\n
\n
Acknowledgments
\n
This work was partially supported by the MINECO/FEDER under the TEC2015-64718-R and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the P11-TIC-7103 Excellence Project.
\n
\n',keywords:"neuroimaging, VBM, feature extraction, CT, MRI, PET, SPECT, machine learning, classification",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/52202.pdf",chapterXML:"https://mts.intechopen.com/source/xml/52202.xml",downloadPdfUrl:"/chapter/pdf-download/52202",previewPdfUrl:"/chapter/pdf-preview/52202",totalDownloads:1364,totalViews:312,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,dateSubmitted:"March 28th 2016",dateReviewed:"July 21st 2016",datePrePublished:null,datePublished:"December 7th 2016",dateFinished:null,readingETA:"0",abstract:"This chapter is intended to provide an overview to the most used methods for computer-aided diagnosis in neuroimaging and its application to neurodegenerative diseases. The fundamental preprocessing steps, and how they are applied to different image modalities, will be thoroughly presented. We introduce a number of widely used neuroimaging analysis algorithms, together with a wide overview on the recent advances in brain imaging processing. Finally, we provide a general conclusion on the state of the art in brain imaging processing and possible future developments.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/52202",risUrl:"/chapter/ris/52202",book:{slug:"computer-aided-technologies-applications-in-engineering-and-medicine"},signatures:"Francisco J. Martínez-Murcia, Juan Manuel Górriz and Javier\nRamírez",authors:[{id:"17445",title:"Dr.",name:"Javier",middleName:null,surname:"Ramírez",fullName:"Javier Ramírez",slug:"javier-ramirez",email:"javierrp@ugr.es",position:null,institution:null},{id:"187376",title:"M.Sc.",name:"Francisco J.",middleName:null,surname:"Martínez-Murcia",fullName:"Francisco J. Martínez-Murcia",slug:"francisco-j.-martinez-murcia",email:"fjesusmartinez@ugr.es",position:null,institution:{name:"University of Granada",institutionURL:null,country:{name:"Spain"}}},{id:"187381",title:"Prof.",name:"Juan Manuel",middleName:null,surname:"Górriz",fullName:"Juan Manuel Górriz",slug:"juan-manuel-gorriz",email:"gorriz@ugr.es",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Preprocessing",level:"1"},{id:"sec_2_2",title:"2.1. Spatial normalization or registration",level:"2"},{id:"sec_2_3",title:"2.1.1. The MNI space and template",level:"3"},{id:"sec_3_3",title:"2.1.2. Registration algorithms",level:"3"},{id:"sec_4_3",title:"2.1.3. Co-registration",level:"3"},{id:"sec_6_2",title:"2.2. Smoothing",level:"2"},{id:"sec_7_2",title:"2.3. Functional MRI-specific steps",level:"2"},{id:"sec_7_3",title:"2.3.1. Slice-timing correction",level:"3"},{id:"sec_8_3",title:"2.3.2. Motion correction",level:"3"},{id:"sec_10_2",title:"2.4. Intensity normalization",level:"2"},{id:"sec_11_2",title:"2.5. Segmentation",level:"2"},{id:"sec_11_3",title:"2.5.1. Statistical parametric mapping",level:"3"},{id:"sec_12_3",title:"2.5.2. FMRIB software library",level:"3"},{id:"sec_13_3",title:"2.5.3. FreeSurfer",level:"3"},{id:"sec_16",title:"3. Basic analyses",level:"1"},{id:"sec_16_2",title:"3.1. Analysis of regions of interest (ROIs)",level:"2"},{id:"sec_16_3",title:"3.1.1. Cortical thickness",level:"3"},{id:"sec_18_2",title:"3.2. Voxel-wise analyses",level:"2"},{id:"sec_18_3",title:"3.2.1. Statistical parametric mapping (SPM)",level:"3"},{id:"sec_19_3",title:"3.2.2. Voxel-based morphometry (VBM)",level:"3"},{id:"sec_20_3",title:"3.2.3. Voxels as features (VAF)",level:"3"},{id:"sec_23",title:"4. Advances in brain image analysis",level:"1"},{id:"sec_23_2",title:"4.1. Classification in neuroimaging",level:"2"},{id:"sec_23_3",title:"4.1.1. Support vector machines (SVM)",level:"3"},{id:"sec_24_3",title:"4.1.2. Ensembles of classifiers",level:"3"},{id:"sec_26_2",title:"4.2. Image decomposition",level:"2"},{id:"sec_26_3",title:"4.2.1. Principal component analysis (PCA)",level:"3"},{id:"sec_27_3",title:"4.2.2. Independent component analysis (ICA)",level:"3"},{id:"sec_29_2",title:"4.3. Other feature extraction techniques",level:"2"},{id:"sec_29_3",title:"4.3.1. Texture analysis",level:"3"},{id:"sec_30_3",title:"4.3.2. Spherical brain mapping (SBM)",level:"3"},{id:"sec_31_3",title:"4.3.3. Artificial neural networks (ANN)",level:"3"},{id:"sec_34",title:"5. Conclusions",level:"1"},{id:"sec_35",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Penny W, Friston K, Ashburner J, Kiebel S, Nichols T, editors. Statistical Parametric Mapping: The Analysis of Functional Brain Images. 1st ed. 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Magnetic Resonance Imaging. 2000;18(1):89–94. DOI: 10.1016/S0730-725X(99)00102-2.'},{id:"B9",body:'Ryali S, Supekar K, Abrams DA, Menon V: Sparse logistic regression for whole-brain classification of fMRI data. Neuroimage. 2010;51(2):752–764. DOI: 10.1016/j.neuroimage.2010.02.040.'},{id:"B10",body:'Dickerson BC, Goncharova I, Sullivan MP, Forchetti C, Wilson RS, Bennett DA, Beckett LA, de Toledo-Morrell L: MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiology of Aging. 2001;22(5):747–754. DOI: 10.1016/S0197-4580(01)00271-8.'},{id:"B11",body:'Gorriz JM, Ramirez J, Lassl A, Salas-Gonzalez D, Lang EW, Puntonet CG, Alvarez I, Lopez M, Gomez-Rio M: Automatic computer aided diagnosis tool using component-based SVM. In: 2008 IEEE Nuclear Science Symposium Conference Record; October 19–25, 2008; Dresden, Germany. IEEE; 2008. p. 4392–4395. 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IEEE Transactions on Medical Imaging. 2016;35(5):1252–1261. DOI: 10.1109/TMI.2016.2548501.'},{id:"B38",body:'Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD: Deep learning for neuroimaging: a validation study. Frontiers in Neuroscience. 2014;8(00229). DOI: 10.3389/fnins.2014.00229.'},{id:"B39",body:'Suka HI, Lee SW, Shen D: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage. 2014;101:569–582. DOI: 10.1016/j.neuroimage.2014.06.077.'},{id:"B40",body:'Ortiz A, Martínez-Murcia FJ, García-Tarifa MJ, Lozano F, Górriz JM, Ramírez J. Automated diagnosis of Parkinsonian syndromes by deep sparse filtering-based features. In: Innovation in Medicine and Healthcare 2016; Springer International Publishing; 2016. p. 249–258. 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NeuroImage. 2013;65(15):449–455. DOI: 10.1016/j.neuroimage.2012.10.005.'},{id:"B47",body:'Chang H, Fitzpatrick JM: A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities. IEEE Transactions on Medical Imaging. 2002;11(3):319–329. DOI: 10.1109/42.158935S.'},{id:"B48",body:'Weiskopf N, Suckling J, Williams G, Correia MM, Inkster B, Tait R, Ooi C, Bullmore ET, Lutti A: Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Frontiers in Neuroscience. 2013;7. DOI: 10.3389/fnins.2013.00095.'},{id:"B49",body:'Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B: A hybrid approach to the skull stripping problem in MRI. Neuroimage. 2004;22(3):1060–1075. DOI: 10.1016/j.neuroimage.2004.03.032.'},{id:"B50",body:'Dale AM, Fischl B, Sereno MI: Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage. 1999;9(2):179–194. DOI: 10.1006/nimg.1998.0395.'},{id:"B51",body:'Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2022;33(3):341–355. DOI: 10.1016/S0896-6273(02)00569-X.'},{id:"B52",body:'Xu L, Groth KM, Pearlson G, Schretlen DJ, Calhoun VD: Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Human Brain Mapping. 2009;30(3):711–724. DOI: 10.1002/hbm.20540.'},{id:"B53",body:'Bossa M, Zacur E, Olmos S, ADNI: Tensor-based morphometry with stationary velocity field diffeomorphic registration: application to ADNI. Neuroimage. 2010;51(3):956–969. DOI: 10.1016/j.neuroimage.2010.02.061.'},{id:"B54",body:'Artaechevarria X, Munoz-Barrutia A, Ortiz-de-Solorzano C: Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Transactions on Medical Imaging. 2009;28(8):1266–1277. DOI: 10.1109/TMI.2009.2014372.'},{id:"B55",body:'Salas-Gonzalez D, Górriz JM, Ramírez J, López M, álvarez I, Segovia F, Chaves R, Puntonet CG: Computer-aided diagnosis of Alzheimer’s disease using support vector machines and classification trees. Physics in Medicine and Biology. 2010;55(10):2807. DOI: 10.1088/0031-9155/55/10/002.'},{id:"B56",body:'Ramírez J, Górriz JM, Segovia F, Chaves R, Salas-Gonzalez D, López M, álvarez I, Padilla P: Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neuroscience Letters. 2010;472(2):99–103. DOI: 10.1016/j.neulet.2010.01.056.'},{id:"B57",body:'Hyvärinen A, Oja E: Independent component analysis: algorithms and applications. Neural Networks. 2000;13(4–5):411–430. DOI: 10.1016/S0893-6080(00)00026-5.'},{id:"B58",body:'Lee TW, Girolami M, Sejnowski TJ: Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation. 1999;11(2):417–441. DOI: 10.1162/089976699300016719.'},{id:"B59",body:'Haralick RM: Statistical and structural approaches to texture. Proceedings of the IEEE. 1979;67(5):786–804. DOI: 10.1109/PROC.1979.11328.'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Francisco J. Martínez-Murcia",address:null,affiliation:'
Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
'},{corresp:"yes",contributorFullName:"Juan Manuel Górriz",address:"gorriz@ugr.es",affiliation:'
Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
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1. Introduction
This chapter will try and help general practitioners master minor surgical procedures.
General practitioners require these procedures for diagnostic or therapeutical reasons, in the outpatient setting as well in the emergency (excision of skin lesions or wound suturing for example). For that reason, the training of the general doctors in minor surgery is an additional tool for good medical practice and acquiring skills in minor surgical procedures has become a critical part of medical training.
Minor surgical procedures do not involve very sophisticated devices. However, some basic requirements in terms of infrastructure and equipment must be met [1, 2].
It is recommended that each facility has a specific room for these procedures. This room (Figure 1) must include:
Figure 1.
Well-equipped room of minor surgery.
Surgical room: a well-ventilated room, with a suitable temperature, it is imperative that is clean, but it does not require sterile isolation. The surgical room should be cleaned properly at the end of the surgical session, particularly after contaminated procedures (e.g. abscesses).
Operating table: It should be easily accessible from all sides, Height-adjustable and articulated tables. It is essential that allows the doctor to work in comfort, both standing and sitting.
Doctor’s stool: A height-adjustable stool on wheels.
Side table: it is used to place the surgical instruments and material used during the surgery.
Lamp: It is necessary to have a directional light source, and it must provide adequate lighting with, at least, 45,000 lux of illuminance. It is advisable to have another auxiliary lamp with a magnifying glass.
Showcase and containers: For storing consumables and surgical instruments. There should also be properly marked containers for bio contaminated material, and a disposal system in accordance with current health legislation.
Resuscitation equipment: Including material for vascular access, airway intubation, saline, drugs for resuscitation (e.g. epinephrine, atropine, bicarbonate) and a defibrillator.
2. Sterilization system
2.1 Physician’s preparation for minor surgery
Performing minor surgical procedures carries some risk of transmission of infectious diseases (such as HCV and HIV), both from patient to doctor and vice versa. To minimize this risk, all physicians performing invasive procedures should adopt and apply universal precautions, which include:
Surgical attire: surgical shirts and trousers (“scrubs”) or gowns and sterile gloves. Surgical masks and eye goggles is considered highly desirable but not essential. Disposable gowns are very useful.
Hand washing: Hygienic scrubbing is suitable for minor surgery and involves using a normal soap solution (no brush) and washing thoroughly all skin folds for at least 20 seconds. Time span from scrubbing to glove placement should never exceed 10 minutes.
Sterile glove placement: Outer surface of the glove should be sterile, therefore they cannot be touched with the hands, only with the other glove; nonetheless, the inner or powdered part of the glove can be touched.
3. Surgical instruments (handling) and suture material
3.1 Surgical instruments for minor surgery
The quality, condition and type of instruments used in any procedure can affect its outcome. Choosing the right instruments for each surgical intervention is, therefore, an important issue [1].
Scalpel: A number 3 handle with leaves number 15 for dissection and 11 for incisions and withdrawal of points. The scalpel blade is installed on the handle in a unique position, matching the blade guide with the handle guide. The scalpel is handled with the dominant hand like a pencil (Figure 2), allowing small and precise incisions. To increase precision, hand should be partially supported on the working surface. Skin should be tightened perpendicularly to the direction of the incision using the contralateral hand, cutting the skin perpendicularly. In hairy areas (eyebrows or scalp), to avoid damaging the follicles, the incision should be parallel to the hairshafts.
Figure 2.
Correct way of managing of the scalpel.
Scissors: The scissors allows us both the cutting dissection of the tissues and the blunt dissection.
A 14 cm long curved blunt May scissors (cutting scissors) and an 11.5 cm curved blunt Metzenbaum scissors (dissecting scissors) should be available.
Scissors are handled by inserting the distal phalange of the thumb and fourth finger into the rings, then supporting the second finger on the branches of the scissors. Usually scissors are inserted with the tip closed and are then opened, separating the tissues in the anatomical layers, except for sharp dissection they are inserted with the tip open, then cutting the tissue.
Needle-holder: needle-holders are meant to hold curved needles while stitching. The needle is held 2/3 of the way back from its point. A small or medium (12–15 cm). Long needle holders are not recommended.
Like other instruments with rings, the needle support is handled equally. To facilitate the passage of the needle through the tissues, the needle holder should describe a prono-supination movement, and for a proper edge eversion of the wound the angle of entry of the needle should be 90°. The non-dominant hand holds the skin with a retractor or dissecting forceps, opposing the pressure of the needle.
Dissecting forceps: Use of a 12 cm-long Adson forceps with teeth to handle the skin, plus a toothless Adson forceps for suture removal or two standard forceps, one with and one without teeth. It is important not to manipulate the skin using non-toothed forceps.
They used with the non dominant hand, between the first, second and third fingers.
They allow the surgeon to expose the tissues to manipulate them.
Homeostats: homeostats are used to pull tissue, for homeostasis and, in some cases, for blunt dissection in absence of small scissors. Usually with 12 cm curved non-toothed Mosquito forceps.
For most minor surgical interventions, a basic set of surgical instruments is enough (Figure 3). But some surgical procedures require the use of special instruments or equipment, such as:
Figure 3.
Basic set of instruments of minor surgery: Scalpel (handle of the number 3 for scalpel number 15), scissors of May, Adson forceps with teeth, needle-holders and mosquito forceps.
Biopsy punch: it is an instrument consisting of a handle and a cylindrical cutting edge (trephine) for obtaining tissue biopsies. It allows the surgeon to obtain full- thickness samples of the skin.
The most useful in minor surgery is the 4 mm punch but they are manufactured in different diameters. They are handled with the dominant hand, performing rotational movements of the instrument to cut the skin and obtain the sample [3].
Curette: it allows scraping of lesions on the skin Surface with a simple surgical technique that involves “scraping” or enucleating different types of superficial, hyperkeratotic or raised partial-thickness skin lesions.
Cryosurgical equipment: these are devices that spray a cryogen, which is usually liquid nitrogen that uses extremely cold temperatures to treat benign and malignant skin lesions (solar lentigines, common warts, myxoid cysts, actinic keratosis, etc.).
It is available, cost-effective, and rapid treatment that rarely requires anesthesia [4].
Electrocautery: it applies an electric current with ability to coagulate and cut through different tissues. There are different terminals depending on the type of procedure that is to be performed [5].
3.2 Suture materials
Different types of suture materials are available: threads, staples, adhesive sutures and tissue adhesives.
Depending on the material used for the suture, the operation time will be modified and will require anesthesia or not.
Conventional sutures require the use of anesthesia, operating time is increased, and tissue is traumatized, but provide a secure wound closure and minimal wound- dehiscence rate compared to other types of closure [6].
3.2.1 Sutures
They are classified according to their origin (natural, such as silk, or synthetic polymers that produce less tissue reaction), their configuration (monofilament or multifilament), and their size (the thickness of the suture is measured using a zero-scale [USP system] (Figure 4). The most commonly used in minor surgery range from 2/0 to 4/0 or 5/0.
Figure 4.
Information on suture: (1) caliber of the thread (system USP and metric), (2) trade name of the suture, (3) composition and physical structure of the thread, (4) length of the thread, (5) color of the thread, (6) model of needle (every manufacturer uses different references), (7) I draw from the needle to scale 1:1, (8) circumference of the needle (expressed in parts of circle), (9) section of the needle, (10) length of the needle, (11) expiry date, (12) indexes of the manufacturer, (13) indicator of sterile packing.
The size and type of suture will be selected depending on the anatomical site, the type of wound and on the patient’s features.
3.2.1.1 Features of main sutures
Nonabsorbable sutures: They are not degraded by the body and they are used for skin wounds in which stitches that are to be removed or for internal structures that must maintain a constant tension (like tendons and ligaments), Polypropylene and Nylon, causes minimal tissue reaction.
Silk: Suitable for skin suture and for removable sutures in general, it is easy to handle and tie.
Nylon: Indicated for precise skin sutures and internal structures that must maintain constant tension.
Polypropylene: Indicated in continuous intradermal skin closure. It is a very soft suture with high package memory and, therefore, it requires more knots for secure tying, and it is more expensive than Nylon.
Absorbable sutures: A suture is considered absorbable if, when placed under the skin surface, it loses most of its tensile strength in 60 days. It has low tissue reactivity, high tensile strength. They are use in dermal suturing, subcutaneous tissue, deep suturing and ligatures of small vessels. The most commonly used, are the synthetic sutures (polyglactin 910 [Vicryl], polyglycolic acid [Dexon]…).
3.2.1.2 Stitch removal
The period of time (in days) recommended for the extraction of points, together with an indication of the type of suture is described in Table 1.
Anatomical region
Skin suturing
Subcutaneous suturing (Vicryl® or Dexon®)
Stitch removal
Adults
children
Scalp
Staples 2/0 silk
3/0
7–9
6–8
Eyelids
6/0 monofilament or silk
—
3–5
3–5
Ears
4/0–5/0 monofilament or silk
—
4–5
3–5
Face, neck, nose, forehead
4/0 monofilament or silk
4/0
4–6
3–5
Lips
4/0 monofilament or silk
4/0
4–6
4–5
Trunk/abdomen
3/0–4/0 monofilament
3/0
7–12
7–9
Back
12–14
14
Lower extremity
3/0 monofilament
3/0
8–12
7–10
Penis
4/0 monofilament
3/0
7–10
6–8
Foot and pulp of fingers
10–12
8–10
Upper limb/hand
8–10
7–9
Mouth and tongue
3/0 Vicryl®
—
—
—
Table 1.
Indications of types of sutures and time for stitch removal.
3.2.2 Suturing needles
Needle selection depends on the type of tissue to be sutured, its accessibility and suture thickness.
Needles are classified as triangular, spatulate or conical, according to their section. Triangular needles are considered the first choice in minor surgery, as they have sharp edges that allow suturing through highly-resistant tissues such as subcutaneous tissue, skin or fascia.
Curved needles are used with the needle holder, that is designed to hold needles atraumatically and safely. Short needle holders are preferred in minor surgery; however, they should be selected in accordance with the size of the needle and the surgical area.
3.2.3 Staples
Staples are applied by disposable staplers and they are available in different widths (R: normal staples, W: Wide staples). Staplers are preloaded with a variable number of staples. It has certain advantages such as the speed with which the suture is performed, low resistance and no tissue reaction.
They are applied with the dominant hand, while the non dominant hand everts the skin edges using dissecting forceps with teeth. Staple removal is performed using a staple extractor.
Indications: In linear wounds on the scalp, trunk and limbs, and for temporary closure of wounds in patients to be transferred or with other serious injuries.
Contraindications: Wounds on face and hands and regions that are going to be studied through CT or MRI.
3.2.4 Adhesive sutures
It consists of adhesive tapes made of porous paper and capable of approximating the edges of a wound or incision. They are available in various widths and lengths, and it can be cut.
Indications: linear and superficial wounds with little tension. The regions where they are used most are: the face, chest, non-articular surfaces of the limbs and fingertips. They are also a good choice for elderly patients and to wound-reinforcement after stitch removal.
Any wound closed with adhesive suture should not be wet for the first few days, due to the risk of tape detachment.
Contraindications: irregular wounds, on the scalp and hairy areas, skin folds and joint surfaces.
Application and removal of adhesive sutures: For a good application the wound should be free of blood or secretions and dry. The suture tape is applied to the wound using dissecting forceps without teeth or fingers, first on one edge of the wound and then the other and along the wound.
Time for adhesive suture removal parallels time for conventional suture.
3.2.5 Tissue adhesives (glues)
These products (cyanoacrylates) act as an adhesive, producing an epidermal plane closure, so they bind the most superficial epithelial layer (stratum corneum) and hold together the wound edges for 7–14 days. After this time, adhesive and stratum corneum are shed along.
Adhesive can be used in deeper wounds or with great tension, associated at sutures in the subcutaneous plane.
It have advantages when compared with sutures: More rapid repair time, less painful procedure, better acceptance by patients, no need for suture removal or follow-up, good cosmetically results. Finally they are safer than sutures because needlesticks are avoided [1, 7].
3.2.5.1 Application technique
After cleanliness and hemostasis of the wound, tissue adhesive will be applied:
Using fingers or dissecting forceps to approximate the wound edges, apply the adhesive on the outer surface of the skin. Then Keep the edges in contact for 30–60 seconds. The process can be repeated 3 times.
The wound does not require dressings but should be kept dry 5 days. The glue will disappear after 7–10 days.
3.2.5.2 Warnings for correct use
If adhesive contact the eyes, use of a generous amounts of ophthalmic antibiotic ointment should be placed within the eye and on the eyelid to break down the adhesive and reopening of eyelids with a gentle manual traction. If adhesive reach the cornea, it should be assessed for corneal abrasion.
4. Surgical procedures and techniques of anesthesia in minor surgery
4.1 Basic surgical maneuvers
The practice of any surgical procedure, however minimal, is not without risks. The possibility of complications during and after surgery must always be kept in mind. The results of surgical treatment are not always predictable, and depend on many factors, involving not only the physician’s skills, but also the patient.
4.1.1 Surgical incision and dissection
There are two ways to dissect tissue: with a blunt dissection, separating the tissue, using Metzenbaum scissors or mosquito forceps, or cutting dissection, with a scalpel or scissors.
4.1.1.1 Incisions shape in minor surgery
Incisions must parallel the minimal tension lines, which match skin relaxation lines and facial expression. Thus, they result in an acceptable scar, both functionally and cosmetically. There are diagrams of the relaxed skin tension lines, for correct incision planning before surgery.
The incision can be marked prior to skin antiseptic preparation or a previously sterilized marking pen can be used in the surgical field after skin preparation and draping.
For excisional biopsies, it is necessary to leave an adequate margin (1–2 mm) of healthy skin both around the lesion and in depth, depending on each lesion.
4.1.1.2 Types of incisions for minor surgery
Incision: Used for drainage of abscesses or surgical exposure of deeper tissues (e.g., epidermal cysts, lipomas, lymph node biopsies). Depending of surgery or the anatomic area, Incisions can be angled, curved or straight.
Elliptical excision: Its should be oriented along the lines of minimal tension.
Usually the length of the ellipse should be 3 times its width and the ends form a 30° angle. Its used to remove skin lesions with a margin of healthy skin in depth and around lesion, and include all skin layers plus some subcutaneous fat (Figure 5). This technique allows diagnosis, treatment and facilitates closure producing good cosmetic results.
Figure 5.
Characteristics of the elliptical excision.
It is the ideal technique to remove the majority of skin lesions [8, 9, 10].
The procedure involves the following steps:
Design of the incision
Preparation of the surgical field
Local anesthetic injection.
Superficial skin incision along the marked ellipse, going through the entire dermis to prevent jagged edges.
Using the nondominant hand the deep wedge-shaped incision is made (always under direct vision), until fat is reached and the lesion is, thus, removed en bloc.
Hemostasis of the surgical area.
Wound closure by layers
Cleaning the surgical area and dressing placement
After 48 hours the wound can be washed gently
Tangential excision: it is the technique of choice to remove very superficial lesions using scalpel or scissors, eliminating only the most superficial layers of the skin and for which diagnosis is certain. The defect created is allowed to heal by secondary intention. Tangential excision also called “skin shave”.
No surgical procedure is complete until the pathology report has been received and the patient informed of the results and prognosis.
4.1.2 Hemostasis
Most episodes of bleeding in minor surgery can be controlled with pressure with a gauze or a surgical towel. It is recommended to apply a compressive bandage on the wound in the immediate postoperative period to reduce hematoma or seroma.
4.1.2.1 Types of hemostasis
Tourniquet: Its allows the exploration of the wound and reduces the surgical time. Its use is limited to distal areas (the fingers nail surgery, etc.) and should not exceed 15 minutes.
The hemostats: The surgeon holds bleeding vessel with the tip of a hemostat without teeth and controls the bleeding. To avoid damaging important structures (for example, tendons or nerves) it is necessary to identify the bleeding vessel.
The ligatures: they are threads that tied around a blood vessel, occlude their light and prevent bleeding. After that, vessel should be fixed with a hemostat. The ligature should pass under the clamp and several knots must be tied.
In the hemostasis by electrocoagulation, the Bovie is used in coagulation mode.
4.1.3 Suture techniques
4.1.3.1 Interrupted sutures
This is the most appropriate for minor surgery, as it helps to distribute stress, and promotes the drainage of the wound. The number of sutures needed varies according to the length, shape and location of the laceration. In general, the sutures are placed away from each other so that no space appears on the edges of the wound.
Simple stitch (percutaneous): It is used alone or in combination with buried stitches in deeper wounds and it is considered the technique of choice.
Simple stitch with buried knot: Used to reduce tension within the wound and approximate the deep planes, before skin suturing. Absorbable material is used, the knot leaving in the depth of the wound, and is cut flush.
Mattress stitch or “U” stitch: It is useful in areas of loose skin (e.g., elbow, back of the hand), where the wound edges tend to invaginate. In addition this suture provides good obliteration of dead space, avoiding the need for buried sutures in shallow wounds.
Horizontal mattress stitch: provides a good eversion of wound edges, especially in areas where the dermis is thick or with high tension [6]
Half-buried horizontal mattress stitch: is used to suture wound angles or surgical edges of uneven thickness.
4.1.3.2 Running sutures
They are contraindicated if an infection is suspected and in very contaminated wounds.
Simple running suture: is a sequence of points with an initial knot and a final knot. It takes a short time to do it, but it makes it difficult to adjust the tension of the skin. It is rarely used in minor surgery.
Continuous intradermal suture (subcuticular): this type of suture allows the wound to be sutured without breaking the skin, avoids the “cross-hatching” and provides an optimal esthetic result. Non-absorbable monofilament suture material or absorbable material can be used. Intradermal sutures are used in wounds where it will be necessary to maintain the suture for more than 15 days. In minor surgery its usefulness is limited.
4.1.3.3 Knot-tying
When a multifilament yarn is knotted (for example, Silk), three loops are usually sufficient (first a double loop plus two simple loops). When knotting a monofilament yarn (e.g., Nylon, polypropylene), an additional loop must be added to increase knot security. The knots should be placed on one side of the wound, rather than placed on top of the incision. This will allow a better visualization of the wound and will interfere less with the healing and facilitate the removal of points.
4.2 Local anesthesia in minor surgery
Local anesthetics block the transmission of nerve impulses and they causing, the absence of sensation in a specific part of the body, also other local senses may be affected.
Local anesthetics can be classified into two groups: esters and amides (lidocaine, mepivacaine, bupivacaine, prilocaine, etidocaine and ropivacaine). For their remarkable safety and efficacy we will only use amides. The association of vasoconstrictors allows better visualization of the surgical field. The most widely used is adrenaline and the maximum dose must not exceed 250 micrograms in adults or 10 micrograms/kg in children [11].
4.2.1 Available presentations
The concentration of the anesthetic is expressed in %. We must know that a concentration of 1% means that 100 ml of the solution contain 1 g of anesthetic. Therefore a 2 ml ampoule of 2% mepivacaine, its contain 40 mg (Table 2).
4.2.2 Use of vasoconstrictors
Due to the risk of necrosis and other alteration like delayed healing, adrenaline should not be used in acral areas (e.g., toes), or in traumatized and devitalized skin.
4.2.3 Basic techniques of local anesthesia
4.2.3.1 Topical anesthesia
It is use in an intact skin and for lacerations and mucosae, especially in children. And their characteristics are shown in the Table 2.
1–3 ml applied directly on wound for 15–30 minutes
Onset 20–30 minutes after application.
Can be effective in children for face and scalp lacerations and less effective in limbs
No important adverse effects reported
For mucosae and acral areas
EMLA® lidocaine 25 mg/ml plus prilocaine 25 mg/ml,
1–2 g of cream should be applied for each 10 cm2 of intact skin and occluded. Maximum dose is 10 g
Onset 60–120 minutes after application. Duration of effect is 30–120 minutes. Not useful on palms of hands and soles of feet
Admitted for procedures on intact skin: scraping and shaving, cryosurgery, electrosurgery, laser hair removal, pre-anesthesia for infiltration
Local mild irritation, contact dermatitis. There have been reports of Methemoglobinemia in children aged <6 months
For wounds or deep tissues
Table 2.
Topical anesthetics used in minor surgical procedures and their characteristics.
4.2.3.2 Infiltration anesthesia
Angular infiltration: From the point of entry, the anesthetic is infiltrated in three or more different directions, like a fan (Figure 6).
Perilesional infiltration: Starting from each point of entry the anesthetic is infiltrated in a single direction. The different points of entry will be forming a polyhedral figure.
Linear infiltration: If the lesion to be operated on is a skin laceration, the anesthetic should be directly infiltrated into the wound edges in a linear fashion. If the wound is bruised and has irregular edges, it is preferable to use a perilesional technique from the uninjured area, and follow along the margins of the wound to avoid introducing microbial contamination.
Figure 6.
Anesthetic angular infiltration: it infiltrates following three or more different directions, like a fan.
4.2.3.3 Loco-regional block
The needle is inserted at the base of the proximal phalanx in a dorsal and lateral location, in the collateral palmar digital nerve, and then local anesthetic is injected (maximum 4 ml). The needle is removed and after aspiration proceeds to infiltrate again the subcutaneous plane.
The surgeon must wait 10–15 minutes to obtain a complete effect of the blockage.
5. Preoperative considerations
5.1 Diagnostic criteria for the most common lesions in minor surgery
It is important that general practitioners have an extensive knowledge of the lesions most frequently treated by minor surgery [12].
The following paragraphs contain an overview of the most important diagnostic consideration in lesions usually treated with minor surgery.
5.1.1 Seborrheic keratoses
These lesions are easily treated with curettage, electrosurgery or cryosurgery. In case of doubt, an incisional biopsy should be sent for histopathological analysis.
5.1.2 Epidermal cysts
They are also known as epithelial cysts, epidermoid cysts, or improperly, “sebaceous cysts.” The cyst wall consists of normal stratified squamous epithelium derived from the follicular infundibulum. Queratin is the main component inside the cyst. Their treatment is surgical removal for cosmetic reasons or due to recurrent infections.
5.1.3 Warts
They are a form of benign epithelial hyperplasia induced by the human papillomavirus (HPV). Clinical presentations of cutaneous HPV infection include:
Verruca Vulgaris or plantar wart: you can use liquid nitrogen or salicylic acid.
5.1.4 Molluscum
It is presents as pearly white papules of 1–5 mm (sometimes even bigger) with central dimpling. They may appear isolated or in groups in the neck, trunk, anogenital area or eyelids. Their first choice treatment is cryosurgery, curettage.
5.1.5 Lipoma
Lipomas are slow-growing benign tumors of mature adipose tissue. They appear as soft, elastic, smooth or multilobulated tumors of variable size, with ill-defined borders, and not adherent to deep planes. The diagnosis is usually made clinically. But ultrasound can be helpful to distinguish a lipoma from an epidermoid cyst or a ganglion cyst [13]. They are generally asymptomatic and they are treated by surgical removal [2].
5.1.6 Fibroma pendulum, skin tags
They are not malignant and their treatment is justified for cosmetic reasons.
5.1.7 Melanocytic nevi
They are acquired lesions in the form of macules or papules or small nodules (<1 cm) and are constituted by groups of melanocytes located in the epidermis, dermis or both areas and rarely in the subcutaneous tissue. Sun exposure contributes to the induction of these lesions.
5.1.8 Actinic keratosis
It is located in sun-exposed areas such as bald scalp, the face, shoulders, ears, neck and the back of the hands. It is caused by damage from exposure to ultraviolet radiation. Actinic keratoses are more prevalent in males of middle-aged.
Actinic keratosis is considered a precancer. 13–25% it could develop into a squamous cell carcinoma.
If lesions are scarce and localized, they may be treated with liquid nitrogen.
5.1.9 Basal cell carcinoma
It is the most common skin malignancy. Approximately 70% of basal cell carcinoma occurs on the face, and 15% presents on the trunk [14]. Exposure to ultraviolet (UV) radiation in sunlight, especially during childhood, is the most important factors that contribute to the development of Basal cell carcinoma.
5.1.10 Squamous cell carcinoma
This is a malignant tumor that usually appears on a previous premalignant lesion and requires a multidisciplinary therapeutical approach involving dermatologists, surgeons, radiotherapists, and chemotherapists [14].
5.1.11 Melanoma
Of all skin malignancies, melanoma has the worst prognosis, Five-year survival rates for people with melanoma depend on the stage of the disease at the time of diagnosis.
5.2 Body areas of risk in minor surgery
High-risk areas for minor surgery include the facial and cervical regions, axillary and supraclavicular regions, wrists, hands and fingers, the groin, the popliteal fossa and the feet.
We must consider those regions with a greater tendency to develop pathological scars (e.g., shoulder, sternal and interscapular region). Also the skin of black patients and children are especially prone.
6. Good clinical practice in minor surgery
6.1 Preoperative
For most basic minor surgical procedures, no preoperative work-up is needed. Table 3 summarizes the precautions of minor surgery in primary care.
-Surgery in the lower extremities in patients with Diabetes Mellitus and peripheral vascular disease. -In patients with arrhythmia, severe hypertension, hyperthyroidism, pheochromocytoma or pregnancy, do not add vasoconstrictor to local anesthetic -Anatomic areas of risk -In patients with chronic use of corticosteroids. Protocol for minor surgery in anticoagulated patients - 3 Day Suspend Sintrom ® - 2 Day Suspend Sintrom ® and add subcutaneous LMWH - 1 Day Suspend Sintrom ® and add subcutaneous LMWH, single dose - 0 Day INR Control. If between 1 and 1.6 proceed to surgery. LMWH single subcutaneous dose. Patient will take the usual dose of Sintrom ® (the same as before the suspension). +1 Day LMWH single subcutaneous dose usual dose of Sintrom ® +2 Day usual dose of Sintrom ® +3 Day LMWH single subcutaneous dose. Usual dose of Sintrom ® +4 Day usual dose of Sintrom ® INR will be obtained on day +10 (seven days after surgery)
Table 3.
Precautions of minor surgery.
In patients with increased anxiety, 5–10 mg oral or sublingual diazepam, or 1–5 mg sublingual lorazepam can be administered 30 minutes before surgery.
Contraindications for minor surgery: Malignant skin lesion, allergy to local anesthetics, pregnancy (surgery should be deferred until the end of pregnancy, if malignancy is suspected, the patient should be referred to a specialist), an acute illness, doubt about patient’s motivations, patients with psychiatric disorders or uncooperative patients or refusal to sign the informed consent form is a contraindication for any minor surgery procedure or technique.
Direct oral anticoagulants [DOACs] (Dabigatran, Rivaroxaban, Apixaban, Edoxaban): If a moderate or high bleeding risk surgery, it can be omitted for approximately 2–3 days before a procedure, and resume 24 hours after surgery. However, cutaneous procedures (e.g., skin biopsy, tumor excision, bone marrow biopsy) generally considered to confer a low risk of bleeding [15].
6.2 Intraoperative complications
Vasovagal syncope is the most frequent complication and is more common in young men. Even some patients lose consciousness.
Treatment consists in administering oxygen and iv. fluids if needed and, in severe cases use atropine (0.5–1 mg sc or iv). Generally, most of patients recover spontaneously over a period of seconds to a few minutes.
6.3 Postoperative complications
Infection can occur in up to 1% of minor surgical patients, symptoms such as fever and/or chills are only rarely seen. Infections are treated by removing some of the stitches, plus daily cleaning and disinfection of the wound and allowing the wound to close by secondary intention. If necessary an oral antibiotic regimen may be initiated and inserted drain into the wound.
Hematoma-seroma: is paramount suturing the wound in layers with no gaps and, applying a compressive bandage to prevent their formation.
Wound dehiscence: After wound dehiscence, repairs will take place by secondary intention.
Hypertrophic scar and keloid scarring.
Conflict of interest
The authors declare no conflict of interest.
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General practitioners should have an optimal infrastructure and medical furniture in a minor surgery operating room. It is important to manage the instruments and materials involved for basic and advanced surgery. Also, for a good clinical practice in minor surgery, it is necessary that general practitioners handle anesthesia techniques (local anesthetic infiltration and regional blocks) and have knowledge of the body areas of risk in minor surgery and the topographic anatomy of the skin for the right performance of surgical procedure. 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Good clinical practice in minor surgery",level:"1"},{id:"sec_48_2",title:"6.1 Preoperative",level:"2"},{id:"sec_49_2",title:"6.2 Intraoperative complications",level:"2"},{id:"sec_50_2",title:"6.3 Postoperative complications",level:"2"},{id:"sec_55",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Arribas JM. Cirugía menor y procedimientos en medicina de familia. 2nd ed. Madrid: Jarpyo Editores; 2006'},{id:"B2",body:'Murphy R, Hague A, Srinivasan J. A review of forehead lipomas: Important tips for the training surgeon. The Surgeon. 2019;17:186'},{id:"B3",body:'Zuber TJ. Punch biopsy of the skin. American Family Physician. 2002;65(6):1155-1158, 1161-1162, 1164'},{id:"B4",body:'Freiman A, Bouganim N. History of cryotherapy. Dermatology Online Journal. 2005;11(2):9'},{id:"B5",body:'Hainer BL. Electrosurgery for the skin. American Family Physician. 2002;66(7):1259-1266'},{id:"B6",body:'Kudur MH, Pai SB, Sripathi H, Prabhu S. Sutures and suturing techniques in skin closure. Indian Journal of Dermatology, Venereology and Leprology. 2009;75(4):425-434'},{id:"B7",body:'Singer AJ, Quinn JV, Hollander JE. The cyanoacrylate topical skin adhesives. The American Journal of Emergency Medicine. 2008;26(4):490-496'},{id:"B8",body:'Hussain W, Mortimer NJ, Salmon PJ. Optimizing technique in elliptical excisional surgery: Some pearls for practice. The British Journal of Dermatology. 2009;161(3):697-698. Epub 2009 Jun 25'},{id:"B9",body:'Czarnowski C, Ponka D, Rughani R, Geoffrion P. Elliptical excision: Minor surgery video series. Canadian Family Physician. 2008;54(8):1144'},{id:"B10",body:'Wu T. Plastic surgery made easy—Simple techniques for closing skin defects and improving cosmetic results. Australian Family Physician. 2006;35(7):492-496'},{id:"B11",body:'Achar S, Kundu S. Principles of office anesthesia: Part I. Infiltrative anesthesia. American Family Physician. 2002;66(1):91-94'},{id:"B12",body:'Wolff K, Johnson RA. Fitzpatrick’s Color Atlas and Synopsis of Clinical Dermatology. 6th ed. New York: El McGraw-Hill Companies, Inc; 2009'},{id:"B13",body:'Rahmani G, McCarthy P, Bergin D. The diagnostic accuracy of ultrasonography for soft tissue lipomas: A systematic review. Acta Radiologica Open. 2017;6:2058460117716704'},{id:"B14",body:'Wang YJ, Tang TY, Wang JY, et al. Genital basal cell carcinoma, a different pathogenesis from sun-exposed basal cell carcinoma? A case-control study of 30 cases. Journal of Cutaneous Pathology. 2018'},{id:"B15",body:'Beyer-Westendorf J, Gelbricht V, Förster K, et al. Peri-interventional management of novel oral anticoagulants in daily care: Results from the prospective Dresden NOAC registry. European Heart Journal. 2014;35:1888'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Jose Maria Arribas Blanco",address:"jarribasb@gmail.com",affiliation:'
Professor of Medicine Department, Faculty of Medicine, Universidad Autónoma de Madrid (UAM), Specialist in Family and Community Medicine, Spain
Specialist in Family and Community Medicine, Servicio Madrileño de Salud (SERMAS), Spain
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The difference between the broken bars and broken end ring segments is experimentally clarified by the Fourier analysis of the stator current. This difference is verified by two-dimensional finite element (FE) analysis that takes into consideration the voltage equation and the end ring. The electromagnetic field in the undamaged motor and the motor with broken bars and broken end ring segments is analyzed. The effect of the number of broken bars and broken end ring segments on the motor performance is clarified. Moreover, transient response is analyzed by the wavelet analysis.",signatures:"Takeo Ishikawa",authors:[{id:"119231",title:"Prof.",name:"Takeo",surname:"Ishikawa",fullName:"Takeo Ishikawa",slug:"takeo-ishikawa",email:"ishi@gunma-u.ac.jp"}],book:{title:"Induction Motors",slug:"induction-motors-applications-control-and-fault-diagnostics",productType:{id:"1",title:"Edited Volume"}}},{title:"Topology Optimization Method Considering Cleaning Procedure and Ease of Manufacturing",slug:"topology-optimization-method-considering-cleaning-procedure-and-ease-of-manufacturing",abstract:"This chapter proposes a novel topology optimization method for the material distribution of electrical machines using the genetic algorithm (GA) combined with the cluster of material and the cleaning procedure. Moreover, the obtained rotor structure was assumed to consist of the simple shape of PMs in order to consider ease of manufacturing. The rotor structure of a permanent magnet (PM) synchronous motor is designed and manufactured. The optimized rotor has 32% more average torque than that of the experimental motor with the same stator. The effectiveness of the proposed method is verified.",signatures:"Takeo Ishikawa",authors:[{id:"119231",title:"Prof.",name:"Takeo",surname:"Ishikawa",fullName:"Takeo Ishikawa",slug:"takeo-ishikawa",email:"ishi@gunma-u.ac.jp"}],book:{title:"Optimization Algorithms",slug:"optimization-algorithms-methods-and-applications",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"112971",title:"Prof.",name:"Germano",surname:"Lambert-Torres",slug:"germano-lambert-torres",fullName:"Germano Lambert-Torres",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/112971/images/system/112971.jpg",biography:"Germano Lambert-Torres is a Professor at the Instituto Gnarus. He received his Ph.D. degree in Electrical Engineering from the Ecole Polytechnique de Montreal, Canada, in 1990. From 1983 to 2012, he was with the Electrical Engineering Department, Itajuba Federal University (UNIFEI), where he was also the Dean of the Research and Graduate Studies, from 2000 to 2004. Since 2010, he has been the Director of R&D, PS Solucoes, Itajuba. He also serves as a consultant for many utility companies in Brazil and South America, and has taught numerous IEEE tutorials in the USA, Europe, and Asia. He is the author/editor or coauthor of nine books, more than 30 book chapters, and 50 transactions articles on intelligent systems and nonclassical logic.",institutionString:"Gnarus Institute",institution:null},{id:"112977",title:"Prof.",name:"Luiz Eduardo",surname:"Borges Da Silva",slug:"luiz-eduardo-borges-da-silva",fullName:"Luiz Eduardo Borges Da Silva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Itajubá",institutionURL:null,country:{name:"Brazil"}}},{id:"114647",title:"Dr.",name:"Ouahid",surname:"Bouchhida",slug:"ouahid-bouchhida",fullName:"Ouahid Bouchhida",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University Dr Yahia Fares Medea",institutionURL:null,country:{name:"Algeria"}}},{id:"115806",title:"Dr.",name:"Marcel",surname:"Janda",slug:"marcel-janda",fullName:"Marcel Janda",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Brno University of Technology",institutionURL:null,country:{name:"Czech Republic"}}},{id:"118339",title:"Dr.",name:"Ebrahim",surname:"Amiri",slug:"ebrahim-amiri",fullName:"Ebrahim Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Louisiana State University",institutionURL:null,country:{name:"United States of America"}}},{id:"118340",title:"Prof.",name:"Ernest",surname:"Mendrela",slug:"ernest-mendrela",fullName:"Ernest Mendrela",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Louisiana State University",institutionURL:null,country:{name:"United States of America"}}},{id:"119374",title:"Prof.",name:"Mohamed Seghir",surname:"Boucherit",slug:"mohamed-seghir-boucherit",fullName:"Mohamed Seghir Boucherit",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"École Nationale Polytechnique d'Oran",institutionURL:null,country:{name:"Algeria"}}},{id:"119375",title:"Prof.",name:"Abederrezzek",surname:"Cherifi",slug:"abederrezzek-cherifi",fullName:"Abederrezzek Cherifi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"119585",title:"Dr.",name:"Ondrej",surname:"Vitek",slug:"ondrej-vitek",fullName:"Ondrej Vitek",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Brno University of Technology",institutionURL:null,country:{name:"Czech Republic"}}},{id:"119587",title:"Prof.",name:"Vitezslav",surname:"Hajek",slug:"vitezslav-hajek",fullName:"Vitezslav Hajek",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Brno University of Technology",institutionURL:null,country:{name:"Czech Republic"}}}]},generic:{page:{slug:"careers-at-intechopen",title:"Careers at IntechOpen",intro:"
Our business values are based on those any scientist applies to their research. The values of our business are based on the same ones that all good scientists apply to their research. We have created a culture of respect and collaboration within a relaxed, friendly, and progressive atmosphere, while maintaining academic rigour.
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Openness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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What makes IntechOpen a great place to work?
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
\n\n
If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. 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I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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