Vegetation indices that were used derived from the images.
\r\n\tSolar radiation is the radiant energy that originated from the sun in the form of electromagnetic radiation at various wavelengths. Solar radiation is the source of renewable energy and can be captured and converted into various forms of energy (e.g. electricity and heat) using different technologies.
\r\n\tA very vast amount of solar energy reaches the atmosphere and surface of the earth and solar energy has been used for heating purposes for a very long-time and after solar cells’ invention in 1954, solar cells have also been used widely for electricity generation. Solar cells convert the sunlight into electricity by the creation of voltage and electric current through the so-called photovoltaic effect.
\r\n\tPhotovoltaic (PV) solar energy has attracted significant attention in the recent decade as a reliable source for power generation due to various merits such as the free source of energy, abundant materials resources, environmentally friendly and noise-free, longtime service life, requiring low maintenance, technological advancements, market potential, and very importantly, low cost. The growth of using photovoltaic (PV) solar energy as a promising renewable energy technology, is being increased more and more worldwide. Therefore, much further research is needed for possible future developments in the field of solar photovoltaic energy.
\r\n\tThe aim of this book is to provide detailed information about solar radiation as the source of photovoltaic (PV) solar energy for a broad range of readership including undergraduate and postgraduate students, young or experienced researchers and engineers.
\r\n\tThis should be accomplished by addressing the various technical and practical aspects of solar radiation fundamentals, modeling and the measurement for photovoltaic (PV) solar energy applications.
\r\n\tThe majority of this book should describe the basic, modern, and contemporary knowledge and technology of extraterrestrial and terrestrial solar irradiance for photovoltaic (PV) solar energy.
\r\n\tThe book covers the most recent developments, innovation and applications concerning the following topics:
\r\n\t• Fundamental of solar radiation and photovoltaic solar energy
\r\n\t• Solar radiation and photovoltaic solar energy potential
\r\n\t• Solar irradiance measurement: techniques, instrumentation and uncertainty analysis
\r\n\t• Solar radiation modeling for photovoltaic solar energy applications
\r\n\t• Solar monitoring and data quality assessment
\r\n\t• Solar resource assessment and photovoltaic system performance
\r\n\t• Solar energy and photovoltaic power forecasting
\r\n\tThese are accompanied with other useful research topics and material.
",isbn:"978-1-83968-859-1",printIsbn:"978-1-83968-858-4",pdfIsbn:"978-1-83968-860-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"4c3d1319d7286e81bfb15c1f4b20460a",bookSignature:"Dr. Mohammadreza Aghaei",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9862.jpg",keywords:"Solar Radiation Modeling, Solar Data Assessment, Solar Monitoring, Solar Radiation Forecasting, Solar Irradiance Measurements, Solar Instruments, Solar Spectral Distributions, Uncertainty Analysis, Solar Cell Technologies, Photovoltaics (PV), Solar Resource Assessment, Photovoltaics Power Forecasting",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 17th 2020",dateEndSecondStepPublish:"October 15th 2020",dateEndThirdStepPublish:"December 14th 2020",dateEndFourthStepPublish:"March 4th 2021",dateEndFifthStepPublish:"May 3rd 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A senior researcher in the field of photovoltaic solar energy, a postdoctoral scientist at Eindhoven University of Technology (TU/e), Chair of the WG2: reliability and durability of PV in EU COST PEARL PV.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"317230",title:"Dr.",name:"Mohammadreza",middleName:null,surname:"Aghaei",slug:"mohammadreza-aghaei",fullName:"Mohammadreza Aghaei",profilePictureURL:"https://mts.intechopen.com/storage/users/317230/images/system/317230.jpg",biography:"Mohammadreza Aghaei is a senior researcher in the field of photovoltaic solar energy, Eindhoven University of Technology (TU/e), The Netherlands. He is chair of the Working Group 2: reliability and durability of PV in European Cooperation in Science and Technology, COST Action PEARL PV.\nHe received the M.S. degree in electrical engineering from the Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, in 2013, and the Ph.D. degree in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2016.\nHe was a Postdoctoral Scientist with Fraunhofer ISE and Helmholtz-Zentrum Berlin (HZB)-PVcomB, Germany, in 2017 and 2018, respectively. He is a Guest Scientist with the Department of Microsystems Engineering (IMTEK), Solar Energy Engineering, University of Freiburg since 2017. He is currently a Postdoctoral Scientist with the Design of Sustainable Energy Systems Group, Eindhoven University of Technology (TU/e), The Netherlands. He has authored numerous publications in international refereed journals, book chapters, and conference proceedings. The main his research interests include Solar Energy, Photovoltaic systems, PV monitoring, LSC PV, solar cells, machine learning, and UAVs.\nDr. Aghaei is a member of the International Energy Agency, PVPS program-Task 13 and International Solar Energy Society, and also an MC member in EU COST Action PEARL PV.",institutionString:"Eindhoven University of Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Eindhoven University of Technology",institutionURL:null,country:{name:"Netherlands"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"6681",title:"Single Photon Emission Tomography (SPECT) and 3D Images Evaluation in Nuclear Medicine",doi:"10.5772/7056",slug:"single-photon-emission-tomography-spect-and-3d-images-evaluation-in-nuclear-medicine",body:'The target of Nuclear Medicine is to provide information on the distribution of a chosen molecule in space and/or also in time, inside the human body. An image of the distribution, of a molecule of biochemical or physiological importance, within the body in a specific organ, provides information on the functioning of an organ that is valuable for medical diagnosis and for monitoring the response to the treatment of a disease.
The techniques used in Nuclear Medicine involve labelling of a specific molecule with a radioactive atom; a quantity of which is administered to the patient. The labelled molecules follow their specific biochemical pathways inside the body. The atoms used are radioactive and undergo random decay, emitting gamma ray photons. Photons are detected outside the body by the detector -NaJ(Tl) crystal- of the gamma camera.
Imaging by two-dimensional (2D) planar scintigrams has been routinely used since many years. The functional information obtained by Single Photon Emission Tomography (SPECT) is complementary to the planar images, obtained by projections of the organ under investigation. SPECT is an important tool for evaluating the functional status of the human body, emerging information by reconstructing data in slices of the total organ.
Image quality and quantitative accuracy in SPECT can be degraded by some parameters as the effects of photon attenuation and finite spatial resolution of the tomographic gamma camera system.
Volume data in nuclear medicine, by rendering and shading, give the 3 dimensional (3D) description of an organ and carry information of an organ’s surface in angles around it. They are obtained by a sequence of 2D slices reconstructed from projections acquired around a 3D volume organ. Volume visualization obtains volumetric signs useful in diagnosis, in a more familiar and realistic way. Filtering, thresholding and gradient are necessary tools in the production of diagnostic 3D images.
The required input data for creation of 3D surface images is a dataset containing slice images which have been previously reconstructed from the SPECT acquisition data. These slices may be in one of several planes: transaxial, sagittal, coronal or oblique, and the orientation of the input data determine the orientation of the final 3D surface dataset. The 3D reconstruction of external surface of kidneys, lungs, liver, thyroid and heart are described here, focused on the details and precision of the final surfaces through grading.
Organ volumes evaluation using planar imaging technique is a procedure often performed in nuclear medicine but faces difficulties due to structures containing radioactivity, which overlie or underlie the organ of interest. SPECT overcomes these difficulties since structures in the interior of the examined organ are separated in the reconstructed images (Rosenthal et al, 1995).
The presentation of functional volumes and activity concentrations in SPECT requires that a linear and stationary relationship exists, between the apparent activity distribution in the reconstructed image and the true distribution in the organ. However, because of the limited spatial resolution of the gamma camera, and physical processes such as photon scattering and photon attenuation, this relationship is neither linear nor stationary (Gourion & Noll, 2002).
Absolute quantification of the 3D distribution of a radionuclide within a patient has been one of the greatest challenges of Nuclear Medicine. This is difficult due to the fact that nuclear medicine images are degraded by several factors (anatomic, physical, technical factors), which limit the quantitative ability of this modality (Tsui et al, 1994).
SPECT is used in image analysis studies to extract information about areas and volumes or amounts of radioactivity in specific regions of interest. The information that derived from these studies is then applied to aid in clinical diagnoses or even to estimate radiation dosimetry or determination of volume or mass (De Sadeleer et al, 1996, Erdi et al, 1995, Pant et al, 2003). Many methods for precise and accurate measurements of volume and the amount of radioactivity in a specific region of interest are used (Boudraa & Zaidi, 2006, Hansen, 2002a).
The set of SPECT images (slices) is used for the 3D volumetric representation of internal organs of the human body. Image based volume rendering techniques have demonstrated the improvement of rendering quality when 3D organ presentation is based on digital images as SPECT slices and some shape and functional information (Sainz et al 2003). 3D volume representation may reach photorealistic quality taking into consideration the factors that degrade slices data and affect quantitative accuracy (Zaidi, 1996b).
3D images, in nuclear medicine techniques, are created by acquisition data sets in angular planes. The data are acquired by SPECT gamma camera in an arc of 180 or 360 degrees – depending on the size and the position of the studying organ- and multiple slices that can be treated as volume data are produced from the angular projections.
It is often considered that adequate compensation for the effects of physical factors, as non uniform attenuation or distance-dependent spatial resolution, requires data acquired over 2π. However, in many cases the data measured over 2π contain redundant information. Sometimes, 2π acquisition affects the quality due to effect of partially compensating attenuation of the surrounding the organ tissues. Noo & Wagner, 2001 have shown that data acquired only over π can be used to correct for the effect of uniform attenuation in SPECT and Pan et al, 2002 showed that with both non-uniform attenuation and distance- dependent spatial resolution the scanning angle can be reduced from 2π to π.
Thyroid gland is a front superficial organ and best data are collected by a 180 degrees arc acquisition, in a clockwise direction. In the case of kidneys – a posterior organ- data are acquired in a counter clock direction, over π. Similarly, because of heart position in the left part of the chest, the data for reconstruction are acquired in a clockwise direction, from -450 to 1350, that is, the data are acquired over π.
Pan et al, 2001 are referred to mathematical rationales that explain that 2π measurements contain redundant information, as, in the absence of noise and other inconsistencies, the measurements of conjugate views are mathematically identical, i.e.
This is an example of the measurement of the 2D Radon transform p (ξ, φ) of a real function over 2π, where ξ is the detector bin index and φ is the angle acquiring data.
In any cases of human organs, as lungs and liver, that their anatomic location includes front and posterior regions, the 2π geometry during data acquisition and reconstruction is necessary.
It is assumed that the count rate measured at a point on a planar image is equal to the sum of the count rates emerging from all the volume elements along the ray line. In reality a significant number of gamma rays will be scattered or absorbed before reaching the detector. This results in a reduction in the observed count rate at the detector:
where N is the measured count rate when N1 is the count rate which would be measured if there were no attenuation, e is the base of the natural logarithms, d is the thickness of attenuating material through which the gamma rays must pass, and μ (attenuation coefficient) is a constant which depends on the energy of the gamma rays and the type of attenuating material. For 140 KeV gamma rays of Tc99m -the radioisotope more used in Nuclear Medicine- the attenuation coefficient is μ=0.15/cm, for human tissue; because of scatter a value of 0.12/cm. is usually used to correct for attenuation( Rosenthal et al, 1995).
A procedure is used for correcting of the errors in reconstruction introduced by attenuation. The attenuation correction procedure is a first order method. Because of the inaccuracy of the correction procedure it is necessary to use a value less than the theoretically correct value; 0.12/cm is usually best for technetium gamma rays in man (Zaidi, 1996, Zaidi & Hasegawa, 2003).
If attenuation correction is used the system needs to know the extent of the attenuating material that is the edge of the patient. The patient outline can be calculated by the system. A threshold level is calculated, many times, as a percentage of the maximum pixel intensity for the whole planar dataset.
Volume visualization in nuclear medicine is a method of extracting meaningful information from volumetric data using, manipulating and rendering a sequence of 2D slices. Volume data are 3D entities that may have information inside them or at there surface and edges at angular views that can be obtained by 3D surface rendering images in different angular views. During rendering time (Sainz et al 2008), given a new view point, the step is to determine which of the reference (acquired) views contribute to the new one and then if they overlap, combine them to produce the final volume (fig.1).
Distribution of angular acquired data. Reference Views will contribute to the interpolated new view. (from Sainz et al 2008)
3D images are reconstructed by surface rendering techniques. Organ surface volumes in nuclear medicine can be made from the scientific data by outlining (threshold use) structures in the serial slices and after stacking them, surface reconstruction is done. The result of tomographic reconstructions is a set of contiguous or superposed slices in which the basic element is a voxel, since it represents volume, with the third dimension equal to one pixel. Voxels are reorganized and displayed in 3D way by labeling each voxel with information concerning the distance and the angle of its surface. Surface images are created and interpolated for any angle resulting in rotating surface images.
By gathering sufficient volume data, analysis and quantification of 3D images give valuable information in medical diagnosis. Angular measurements on body structures are often crucial for analysis of the data for diagnosis. Shaded surface images provide unique views of the three dimensional radiopharmaceutical distributions within the human body (Shin et al, 2009).
When viewed together with two-dimensional data, 3D surface images give excellent information about the location, orientation and extent of regions of isotope uptake. 3D datasets are very strong visual representations of tomographic data, and are mainly used qualitatively to help orientate the user with the tomographic slice data. This reinforces the three dimensional nature of nuclear data and strengthens the user\'s awareness of what the data represents and he can glean information on perfusion details for liver or cardiac studies and function information of thyroid, lungs or kidneys, from the 3D surface images.
When 3D surface images are generated, data are processed from each slice in order. This set of slice images contains information in the form of count values in volume elements, voxels, which represents the distribution of the radiopharmaceutical within the volume being imaged. The 3D surface images which are produced provide views of a specific three dimensional surface of this volume. This surface is defined by a count threshold which is applied to each of the voxels; all voxels which have count values greater than the threshold define a specific volume. The surface images display the surfaces of this volume. There are 3 possible ways in which 3D datasets can be generated: a) At a fixed count threshold, the view angle of the surface is varied to create the impression of a rotating surface, b) At a fixed viewing angle, the count threshold is varied to create a dataset which sequentially removes layers of counts from the object rather like peeling the layers from the examined organ, c) Using a fixed view angle and threshold, the distance from which the surface is viewed is varied. This enables the user to take slices through the surface image.
Many methods have been developed for image edge detection and most of these techniques work well in images with uniform regions, but less well in regions with greater non uniformity. Medical images are usually characterized by faded features utilizing a narrow distribution of grey-levels. Nuclear medicine images often suffer from low contrast that is further degraded by the noise introduced in the process of imaging (Razifar et al 2005). It is important to use the local contrast information as well as the overall distribution of grey-levels for obtaining meaningful regions. A large number of algorithms with many approaches have been investigated. These approaches include histogram based methods, edge and boundary detection based methods, region growing based methods, and linear feature based methods (Mortelmans et al, 1986, Russ 2006, Murli et al, 2008).
The accurate reconstruction of organ volumes from SPECT images suffers from image segmentation. Image segmentation defines the borders of the organ and allows volume measurements by counting the voxels inside (Zingerman et al 2009). The threshold has to be adapted for each case as it is dependent on the size of the organ and the contrast of the reconstructed SPECT slices.
Some threshold algorithms have been applied to minimize the difference between true and reconstructed volumes. Grey level histograms analysis has been proven a suitable technique for thresholding. Segmentation is performed with algorithms that separate object and background pixels by comparing their intensity (counts) with the mean intensity of the region. A region of interest is roughly defined by the operator to include the slices of the organ to be reconstructed.
Thresholding is one of the simplest and popular techniques for image segmentation. It can be performed based on global information that is grey level histogram of the entire image or using local information of the image. In thresholding method, to obtain automatically the optimum threshold value, an initial estimate for threshold T is selected. The image is segmented using T.(Boudraa & Zaidi, 2006). In this way two regions of pixels will be separated: RI all pixels of the image with grey values > T and RB the pixels of the region considered as background with values<T. By the average grey level values MI and MB for the regions RI and RB a new threshold value is computed:
The main drawback of histogram-based region segmentation is that histogram provides no spatial information. Region thresholding approaches exploit the fact that pixels close to each other have similar grey values. The main assumption of this approach is that regions are nearly constant in image intensity and it is referred as a robust technique for volumetric quantification and localization of abnormal regions.
However, combining intensity and gradient data can improve histogram (Csetverikov, 2007). Better separation of objects and background pixels close to edges, give high gradients; pixels of object and background, though, have low gradients. Then, in order the result be improved, histogram high-gradient pixels are discarded. (fig.2.).
The gradient shading is used to add shading to the 3D reconstructed image Gradient shading enhances the display of surface detail.
Discarding high gradient pixels close to edges improves the reconstructed image. (from Csetverikov, 2007)
The basic reconstruction is created by the planar images, collected by the gamma camera and contain the number of gamma rays detected at each angle position on the face of the collimator. The image which is to be generated is composed of the numbers of gamma rays emitted from each of the small pixels. The data from which this are to be calculated is the sums of the counts in various rows of the pixels drawn at the angles at which images were recorded at acquisition time.
If, for example, 64 planar images in 64 x 64 matrices are acquired 3600 around the patient, each transaxial slice can be reconstructed from the information taken from a single row of elements (pixels) in each planar image. This information is used to calculate the values of 4096 pixels in the transaxial slice. For each angle-each planar image- there are 64 measured values, each measured value being the sum of the numbers which should appear in the reconstructed slice. The reconstruction problem is to calculate a set of numbers for the transaxial matrix the sums of which, in every direction at which a measurement was made, are equal to the measured values at that angle. In the current example there are 4096 equations (64 sums multiplied by 64 angles) containing a total of 4096 unknowns, the numbers in the pixels.
A number of methods have been used to perform reconstructions of this type.
The method of Filtered -Back-Projection (FBP) is composed of two stages; the first is filtering of the planar image data, and the second is to back-project the filtered data to form transaxial images. The back projection is based on the inverse transformation of the geometrical model of the SPECT data acquisition. The algorithm works well for infinite number of projections, but when a limited number of projections are produced the back projection method causes “star pattern” around the reconstructed object. The star-shaped pattern and the noise are reduced by filtering the projections with a FBP algorithm (fig.3).
When the collected data are back projected, the rows with the largest numbers overlap at the position of the source, but not elsewhere. The result is that the back-projected numbers add up over the position of the source into very much larger numbers than those in other areas of the matrix.
Back projections of a point source created by finite numbers of projection angles; in this case "star pattern" occurs.
The difficulty with simple back-projection is that areas of the reconstructed slice which should be zero, as no radioactivity was there, are not zero. In addition there is always an increase in the numbers near the centre of the image. In order to overcome these problems, the original data are necessary to be modified by “filtering” before back-projecting.
The modification procedure is done by filtering. This additional filtering can be performed by pre-filtering the planar data, by filtering the final reconstructed slices, or by combining a suitable filter with the back-projection filter. Because of these, it is convenient to perform the filtering on the Fourier transform of the data, rather than on the data itself.
The filter which is used is a frequency filter. It causes some spatial frequencies to be enhanced and others to be reduced. Correction for the central build-up in back-projection requires that low spatial frequencies be attenuated and high spatial frequencies be amplified.
The filter which exactly corrects for the back-projection defects is the Ramp filter (fig.4). This filter enhances high frequencies by multiplying all frequencies by a term proportional to the frequency. This "Ramp filter" compensates for the distortion introduced by back-projection but takes no account of the nature of the information being back-projected. The ramp filter may be applied in two ways; one takes account of several mathematical considerations, producing quantitative results and the other produces results with improved image contrast
High frequency information is greatly magnified by the Ramp filter. Hanning filter drops sharply to zero at a frequency (0.8 cycle/cm, in this figure) close to the limiting resolution of the gamma camera.
Radioactive decay is a random process, so all gamma camera data is limited by counting statistics. The statistical variations which always exist between one pixel and the next tend to disguise the true count rate being measured. This statistical noise can vary rapidly from one pixel to another according to Poisson statistics, and so contains high spatial frequencies. The information contained in these high frequencies is not real information about the patient, but contains “noise”. This statistical noise is not wanted, but is always present in gamma camera images. Although it may be necessary to tolerate statistical noise in most images, there is no desire to increase the amount of noise. If tomographic data is filtered using the Ramp filter, all high frequency information is greatly magnified, and so therefore is the statistical noise (fig.4). For this reason the filters used in tomography are usually designed to drop sharply to zero at a frequency which corresponds to the limiting resolution of the gamma camera. Two general classes of such filters are provided, in addition to the ramp filter. The user may choose the limiting frequency at which the filter goes to zero as by Hanning filter (fig.4) or the relative height of the filter in the mid-frequencies when it starts to approach zero by Butterworth filter. Kao & Pan, 2000, have described other non-iterative methods that suppress image noise and artifacts in 3D SPECT.
Iterative reconstruction methods are also used to obtain images with good signal-to-noise ratio in nuclear medicine image reconstruction. Maximum Likelihood Expectation Maximization (ML-EM) method reduces artifacts but requires more computation time. Another iterative filtering method the Ordered Subsets Expectation Maximization (OSEM) is used in 3D SPECT reconstruction and compared to FBP. OSEM and FBP presented similar accuracy in volume measurements of myocardial perfusion (Daou et al, 2003). However, it is possible to calculate the noise constructed by FBP while this not possible by iterative methods (Hansen, 2002b). ROC analysis of OSEM and FBP techniques in lung cancer, showed that there was no significant difference in the area under the ROC curve (Son et al, 2001).
The imaged volume data which will be used for surface image generation is obtained from a set of transaxial, sagittal, coronal, or oblique slice images. Prior to the generation of the surface images, processing operations should be performed.
To improve the resolution of acquired data the maximum possible matrix size both for data acquisition and processing is used. The visual quality of the surface images improves as the voxel size decreases. Therefore, a 128 x 128 resolution acquisition would be preferred and is used for acquiring data from organs as kidneys or thyroid gland. In practice, however, many times a 64 x 64 acquisition is used with excellent results; The use of the 64 x 64 matrix offer the advantage of reduced storage requirements and processing times. Data from organs as myocardium or lungs are usually acquired in a 64x64 matrix size.
Filters used in reconstruction are also very important for the maximum transfer of information in 3D images. The choice of the filter is dependent upon the noise characteristics of the data (Yan and Zeng 2008). Since the images depict a surface, an increased amount of smoothing may be desirable without loosing surface special features, as roughness or smoothness, which may be characteristics of an abnormality of the organ. The appearance of defects at parts of the organ that are not functioning is crucial too. Lungs emboli follow up by imaging, for the perfusion improving or renal pelvis imprint are signs for the diagnosis and must be emerged in surface 3D images. A good general purpose filter for this application is the Hanning 0.8 prefilter, with a ramp backprojection filter. For high resolution data such as thyroid studies, a Butterworth filter which passes more of the middle frequencies can be used.
Zoomed reconstruction technique is used to perform a 3D zoom in the transaxial space in cases of small organs as pediatric kidneys or thyroid gland. Any tomographic slice dataset may be magnified in 3 dimensions, following reconstruction. Three dimensional magnification is recommended as it reduces the voxel size, providing improved visual image quality. Since the output images have the same resolution in pixels as the input images, magnification is limited by the possibility of excluding volumes of interest.
A magnification by a factor of 2 in all three directions is common in small organs. For example, for an initial set of 32 transaxial slices of 64x64 resolution data, as in the case of a myocardium SPECT acquisition in an 1800 arc, the portions of the transaxial images within a certain window are magnified by double. In addition, 31 new magnified transaxial slice images are created in between each of the magnified original images. The resulting dataset will contain 63 transaxial slice images of 64 x 64 data.
It is possible to magnify the set of slices which is the result of a previous magnification, to obtain an over-all magnification factor of 4. In this study, myocardium perfusion transaxial slice data is normally magnified by a factor of 2 prior to 3D surface image generation.
In cases where multiple structures exist in the imaged volume, it may be useful to remove data which does not belong to the structure of interest by selectively remove unwanted structure from the slice images by setting selected voxel values to zero. This operation has applications in lung studies, to separate the right and left lungs or in studies of the liver where the spleen may be useful to be zeroed out. It is, also, sometimes, useful to apply in kidneys 3D surface images creation, in cases that the function of the parenchyma of one kidney is very deteriorated and the maximum intensity of one kidney is lower than 50% of the other kidney. In this case, two regions of interest are defined for the two kidneys and are processed separately. Then each kidney is reconstructed, determining the threshold as percentage of the its maximum.
3D Surface images are created from tomographic slice data in two steps in which the images are shaded. By distance shading operation, the distance shaded surface images are produced. The detection of the surfaces occurs and this accounts for most of the processing time. The surface which will be defined for display is the surface of a volume which is obtained using a calculated count threshold. This volume contains all voxels, which have count values which are above the count threshold value. The distance surface images are created to appear as if the volume is being viewed from specific viewing planes. In the creation of 3D surface images of all the studied organs, the data of transaxial slices were used; by this selection, the orientation of the organs in the body (head/up- feet/down) is kept.
A transaxial slice image is displayed, after the application of a threshold intensity/count value so that all voxels which have count values which are greater than the count threshold are white and those below are black. The outer edge of the threshold slice is the desired surface boundary. A distance surface is generated from the entire set of slices.
The 3D image which is displayed is the surface of a volume defined by count threshold. Thus, volume contains all voxels which have count values which are above the count threshold value. For ease in application, the calculated, according to equation (3) threshold level is specified as a percentage of the maximum count level in the input slice dataset.
It is helpful when determining the appropriate threshold value for a given dataset, to create multiple surface images, from one viewing plane, changing only the threshold value from one image to the next, iteratively.
Typical threshold values are usually of the order of 40%-50% for heart, lungs and liver data. For kidneys and thyroid gland data may give a different value, ranging from 30% to 50%.
The distance shaded surface images are created to appear as if the volume is being viewed from specific viewing positions. Each position is more specifically defined as an image which lies in a predefined viewing plane. These viewing planes may be positioned anywhere around the imaged volume, if the vertical axis of the viewing plane is parallel to the axis which is normal to the slice data. As the slice images are of the transaxial type, the viewing planes are positioned to view the data from the same locations as in the original projection/planar views, at any angular position about the axis (Garcia et al, 2001).
The angular position of the 3D surface images depends on the angle steps (usually 16) selected to reconstruct and view the 3D surface images, as an angle measured clockwise from a line which rises vertically from the centre of the image. Thus, if the input slices are of the transaxial type, 0 degrees corresponds to an anterior view, 90 degrees produces a left lateral view, 180 degrees produces a posterior view, and 270 degrees produces a right lateral view. A start distance value equal to zero places the viewing plane outside the entire organ volume for viewing the surface of the organ.
Following, gradient shading is used to add shading to the image produced in distance shading procedure. The amount of gradient shading on a set of surface images is varied, without having to re-compute 3D distance shading images, each time. Gradient shading is expressed in percentage and is depending on the surface functionality which in this way expresses.
Gradient shading is used to enhance the display of surface detail. Garcia-Panyela & Susin, 2002 used surface reconstruction in their dynamic model to provide volume and give functionality keys about the organ. With this type of shading, the surface image brightness is a function of the local slope of the surface. This calculation is carried out on the distance surface images.
The value for the gradient surface image pixel is at a maximum when the local surface element is facing front. As the surface slopes away towards another angle, the gradient image pixel value decreases. In the limit, when the local surface element appears as an edge the gradient image pixel value is zero.
The final 3D surface images produced include the effects of both distance and gradient shading. We have computed 3D gradient shaded images from 3D distance shaded images for all human organs that we are referred in this work. We select the amount of gradient shading which will be added to a distance surface image by a gradient shading factor 10 to 20 depending on the physiology of the organ; that is abnormality of its parenchyma or its perfusion deterioration. The amount of shading to add depends on the black and white or color maps employed. A grey scale map or a color map is used as this is the way that 3D surface images produce best detailed display useful in medical diagnosis.
The imaging system is a GE starcam AT 4000 equipped with a low energy high resolution collimator (LEHR) for acquisition of scintigraphic studies of all the organs. System collimator, used in all our studies, was this LEHR in order to achieve the most detailed data.
The tomography hardware/software gamma camera facility uses the tomography ring stand, to rotate the camera around the patient during acquisition. A very fast, array processor is used to reconstruct the tomographic images. Furthermore, the whole acquisition- processing system is interfaced with a GE Xeleris 2 for fast computing and displaying planar, SPECT and 3D volume images. Elementary voxel size is determined separately. To establish this measurement, an image of two 1mm parallel line sources, 10mm apart, was created and the number of pixels between the center of the two line sources was measured. Rotating the line sources 90 degrees would provide the pixel width in the opposite direction. Pixel size was controlled weekly and in a 64x 64 matrix is approximately 6.4 mm (+-0.2).
The SPECT and 3D reconstructed images of clinical applications of the Heart, Lungs, Liver, Kidneys and Thyroid gland have been exploited and displayed. The application of this technique includes the study of parenchymatous organ (Liver, Kidneys, Thyroid gland) for possible abnormalities or for the potential assessment of organ perfusion (Heart, Lungs) in a three dimensional display. Volume images are rotated on any spatial plane and provide rich detailing of the organ surface and functional completeness. Angular appearance of body structures is often crucial for determining the medical diagnosis.
Tc-99m DiMercaptoSuccinic Acid (Tc-99mDMSA) renal scan is the method of choice for the detection and follow-up of any possible cortical damage to the kidneys. The test is widely performed in children in order to check for any possible signs of acute pyelonephritis, scars as well as for any suspected dysplasia of the kidneys (Temiz et al 2006). Kidneys’ are located in the posterior abdomen in a very small depth depending of the size, weight and age of the patient.
The usual procedure involves intravenous injection of Tc99m-DMSA and acquisition at 4 to 6 hours post-injection, of four planar images: Posterior (POST), Anterior (ANT), Right Posterior Oblique (RPO) and Left Posterior Oblique (LPO). The planar imaging (renal scanning) of the patients is followed by a SPECT study (Groshar et al, 1997, Yang et al, 2001).
Tomographic imaging was carried out by data acquired in 15 sec at each of 32 positions over 180 degrees around the posterior view of the patient. The 32 angular projection views – the original data as recorded by the gamma camera- are reconstructed to get SPECT slices –transaxial, coronal and sagittal-. An acquisition magnification equal to 2 was used for planar as well as angular projections for the SPECT study. Acquisition matrix size 128x128 was used in all cases and a post reconstruction magnification equal to 2 was used in paediatric cases. In this way, 3D surface images of paediatric kidneys’ were created by a final 4fold magnification to obtain the best spatial detail. FBP, Hanning with crucial frequency 0.8 and ramp filter were used in renal reconstructions. Sheehy et al, 2009, compare two filtering methods (OSEM and FBP) of reconstructing renal SPECT studies and noted that both techniques yielded identical findings for 94 of the 98 kidneys evaluated.
Lyra et al 2001, use the planar and tomographic reconstructed images for the calculation of three indices that are the ratios of counts of upper to lower, upper to middle and lower to middle part by a tomographic reconstruction technique "by parts" and they offer a quantitative comparison of the planar and tomographic images.
3D surface images have not been evaluated up to now in an angular display of surface images either as normal or pathological 3D images, where scars, renal size and renal pelvis imprint can be identified. Following figures (fig. 5, 6, 7, 8) are examples of normal and abnormal situations of the kidneys in which different qualitative signs are emerged.
surface images of the normal kidneys of a child 1 year old, reconstructed for 9 angles around the body. Notice a tiny impress of right renal pelvis-Physiological sign-.
3D surface images at angular positions of a 6 months old renal study, in grey scale. Physiological impress of both renal pelvis. Reduced functioning parenchyma of the lower pole of right kidney
surface reconstructed angular images of kidneys; Deficiency of 18% of the upper pole of left kidney (scar) and central pyelocalyceal system –pyelonephritis-.
3D surface images at 4 angles; Compensatory hypertrophy of left kidney with important deficiency of the parenchyma close to pyelocalyceal system; Atrophic right kidney, with 22% of total functioning renal parenchyma.
3D display clearly offers additional valuable information. The procedure does not require any extra quantity of radiopharmaceutical to be injected to the patient, therefore the radiation burden is the same. The extra time required is in the order of 15 minutes, therefore not presenting any inconvenience for the patient.
Heart scintigraphy provides information with respect to the detection of myocardial perfusion defects, the assessment of the pattern of defect reversibility and the overall detection of Coronary Artery Disease (CAD). There is a relationship between the location and the degree of the stenosis in coronary arteries and the observed perfusion on the myocardial scintigraphy, using data of 3D surface images of myocardium. This allows us to predict the impact evolution of these stenoses to justify a coronarography or to avoid it.
The visual interpretation of Tc99m tetrofosmin SPECT myocardial perfusion images can be challenging, due to the quantity of scan information generated by the large number of normal variants, attenuation artefacts and gender differences.( García-Panyella & Susín 2002) Defects of the cardiac structure can be emerged from the 3D surface images of myocardium. Investigations on phantom studies (Matsunari et al, 2001) have been done and new method for segmentation of left ventricle (LV) for estimation of defects’ size (Soneson et al, 2009) has been validated.
Our Cardiac patients had completed stress (Tc99m tetrofosmin at stress peak) and rest SPECT test by a GE Starcam 4000 tomographic gamma camera, use of 1800 arc rotation, step and shoot, 20 sec per projection and 64x64 matrix size and magnification 2, for data acquisition. The data of the 2 sets (stress-rest) of slices were used to produce 3D surface images of myocardium for 16 angles around the body of the patient. We exploited the myocardial 3D scintigraphic data of the left ventricle, at stress and at rest, in order to recognize the cardiac volume and estimate perfusion defects as a percentage of LV myocardium mass. Co identification of myocardial perfusion images data was performed to eliminate normal morphological variances such as variances in orientation, size and shape, so that the remaining differences represent important functional differences. Dixon et al, 2004 suggest that when attenuation correction and detector resolution compensation are applied during reconstruction, patient variables do not influence the quantitative accuracy. A significant improvement in results was found with zoomed acquisitions.
3D data reconstructed by FBP, obtained at stress and at rest scintigraphic studies, used to evaluate the left ventricle deformation in both stress - rest 3D surface image series. If a significant difference is obtained in rest and stress 3D data perfusion, the location and the impact of the pathology of left ventricle myocardium are recognized. The myocardial defects have been calculated as percentage of the myocardium at rest after the estimation of the total myocardium.
The following 5 next figures (fig 9, 10, 11, 12, 13) are referred to cases that myocardium diagnosis is rely on 3D surface shaded images; 3D data obtained at stress and at rest of the LV myocardium, respectively, are analysed and the deformation of both images is evaluated, qualitatively and quantitatively.
It is expected that further significant improvement in image quality will be attained, which, in turn, will increase the confidence of image interpretation. The development of algorithms for analysis of myocardial 3D images may allow better evaluation of small and non-trans mural myocardial defects. For the diagnosis and treatment of heart diseases the accurate visualisation of the spatial heart shape, 3D volume of the LV and the heart wall perfusion play a crucial role. Surface shading is a valuable tool for determining the presence, extent and location of CAD.
3D shaded surface display of a patient stress perfusion angular images. The study by Tc99m tetrofosmin shows normal myocardium perfusion. (a) for apex and (b) for base of myocardium. Transaxial slices were reconstructed and the created volume images show the apex at the left side. Through base we recognize the cavity of LV. No stress defect was observed and calculated as 0% of the total myocardium.
Small defect at posterior- basal wall at stress (3% of the myocardium). Partial improvement at rest (2% rest defect); Threshold value 50% of maximum.
3D volume myocardium, at Rest. Permanent ischemic findings at inferior basal wall; Rest defect 8% of the total myocardium. Threshold: 50%.
3D surface images of a male myocardium study at Rest. Permanent defect at inferior, inferior-posterior and lateral of LV. Rest defect: 28% of total myocardium volume.
Stress [a] and Rest [b] 3D surface angular images of female myocardium. Low extent permanent scar at apex wall and 4% defect at posterior – basic wall during stress. O% defect at rest.
Patients on the suspicion of hepatocellular disease may complete a liver scan. They are injected intravenously with Tc-99m Phytate and images are acquired 10 minutes post injection. The acquisition involved four planar images and a tomographic study of 64 planar views over a 3600 arc. Each SPECT angle view is preset to end in 20 sec.
3D surface shading angular series images of liver and spleen of a 12 years old boy. Normal volume and smooth surface of both organs.
The tomographic reconstruction is performed with the FBP, Hanning 0.8 and no magnification. An image threshold calculated according to equation 3 for each specific patient and a gradient-shading factor of 20% is applied on the 3D reconstructed angular images.
3D surface images of the liver could be presented together with the planar anterior image and series of coronal views to increase the diagnostic effectiveness of the method, as information of the surface texture and volume size of this large organ together with Spleen position and size express its patho-physiology. Shin et al, 2009 present surface models of detailed structures inside and outside the liver to promote medical simulation system of the abdomen.
3D volume shading images of another normal variant of liver. Notice the impress of the gallbladder location in the middle of the liver, between right and left lobes (2-4 angular images).
3D shading volume images of liver and spleen. Smooth surfaces of the organs. A part of the spleen is only presented (abnormal shape, image 1 and 4) due to rupture of the spleen at a recent car accident.
The signs of the hepatocellular disease (non-homogeneous distribution in liver) are evident in the 3D volume images. Non smoothed surface of the liver (cirrhotic signs) and increased volume size of the spleen.
SPECT of perfusion lung study not only increases the diagnostic accuracy of the method but also permits the application of advanced image-processing techniques (Reinartz et al, 2006, Roach et al, 2008). In the evaluation of the lung status during pulmonary embolism and patient’s follow up, accurate volume estimation is important. Lungs’ 3D volume display, by
Sequential 3D angular images of lungs’ volume in a perfusion study. There is a large triangular defect in anterior basal and medial lobes of the right lung (RL) due to decreased blood perfusion caused by emboli (images 10, 11). Notice the cavity at left lung that accommodate heart (image 12).
reconstruction of SPECT images, can demonstrate the perfusion non-uniformity of the lungs’ parenchyma.
Patterns of 3D perfusion scans with Tc-99m microspheres can provide an estimation of the extent of pulmonary embolism. Measurement of regional distribution of blood flow can help predict the consequences of lung resection or lung reduction surgery and cystic fibrosis, or radiation therapy. Four functional categories of lung pathology can be distinguished: the vascular occlusive state and the consolidative, obstructive, and restrictive states, resulting in scintigraphically detectable distortions of perfusion. Segmental or sub segmental hypo perfusion can be caused by obstruction of pulmonary vessels due to intra-or extra vascular pathology, including perfusion emboli.
3D tomography emerges an anterior basal defect [ 11%], of total volume, at right lung
Small triangular perfusion defect (5%) at the superior lobe of left lung; Similarly, two small perfusion defects at superior and lateral –basal lobes of right lung ( 3% each). Multiple acute lungs embolisms
A SPECT lung study can be used to estimate the lung perfusion improvement in details. Data reconstructed in transverse, coronal, sagittal slices as well as 3D surface images and series of follow up SPECT studies, after the pulmonary embolism event, must be used (Lyra et al, 2008b).
From lung perfusion SPECT data, volume of each reconstructed transverse slice can be estimated by counting the voxels inside each slice. The grey histogram edge detection program mark the lung lobe’s edge and subtract background. Non-uniform attenuation compensation is performed by quantitative FBP.
Follow up 3D studies after the pulmonary embolism event, in order to estimate the lung perfusion improvement, quantitatively.[a] Four hours post the event; Great right lobe (RL) embolism; Right Lobe volume: 23% of total Volume (both lungs) [b] Eleven days post therapy; Right Lobe (RL) volume recovery : 49,6% of total Volume
Single photon emission tomography of the thyroid gland enables improved accuracy over planar imaging in the determination of the volume, regarding the thyroid configuration variants and the difficulty of definition of the organ’s borders over the surrounding background.
The accuracy of the volume estimations depends on the correct delineation of the borders of the thyroid tissue. The thersholding procedure adopted is the grey histogram thresholding and is specific for each case, in a range that start even from 20% of its maximum. Difficulties arise due to various factors including fluctuations in the background and the gland’s shape and function as well as an unavoidable smoothing of the thyroid boundary during reconstruction (Zaidi, 1996a).
Tomographic images at various levels are obtained with each slice 1 pixel thick. That is, the size of the elementary voxel is one pixel in the x and y axis and 1 pixel, too, in the z direction. After correction for the contribution of background, the pixel values in a 2D transverse or coronal slice represent the radioisotope consentration within that slice.
A series of 16 coronal slices covering the thyroid gland thickness and showing the area of each slice.
Influence of attenuation compensation for a superficial small organ as the thyroid gland, seems not significant for volume measurements. The geometry - organ size, shape and location - varies between individuals. Volume surface displays of thyroid gland at angles show the real configuration of the gland’s lobes.
3D thyroid gland images at 16 various angles. Increased the right lobe size and high intensity at its upper pole showing a “hot” node.
Increased left lobe; butterfly shape with very thin isthus, impossible to be seen in planar scan.
A threshold to every single case is adapted as its value is dependent upon size of the imaged organ and the contrast. The thyroid lobes are enclosed with the help of regions of interest (ROI) tools for a first contouring its borders. Size of the organ, however, influences the correct threshold which correspond to a level slightly greater than the maximum fluctuation in the image background. After subtracting this level from the image, the boundary of the thyroid is delineated by the volume elements (voxels) that contain non zero counts; that is the border pixels contain counts equal to the threshold.
Gradient shading was used to surface images, by a gradient factor up to 5% low. Determination of thyroid volume leads to the calculation of the thyroid mass and the accurate activity to be administered for patient specific therapeutic purposes (Pant et al, 2003).
Smoothed surface volume displays. Increased size of right lobe. The two lobes come close by a very thick isthus.
The large toxic adenoma is appeared back to the right lobe. The two lobes could not be formed at all in the planar scan but are emerged in this 3D – low threshold- image.
By gathering sufficient volume data, analysis of 3D images gives valuable information to give the volume, shape and texture abnormalities of an organ (nodules in Thyroid, homogeneity of the liver surface) or defect regions’ (emboli in Lungs, low perfusion in myocardium). Shading according to the gradient of the surface, results in 3D texture surface display, useful in pathologies (e.g. cirrhotic Liver).
Data sets from the above mentioned organs could also be analyzed by image processing software to take new valuable parameters. These images can be transferred in an uncompressed bitmap format and processed by Interactive Data Language (IDL) tools.The volume visualization of nuclear medicine data takes advantage of 3D texture analysis. So, in these 3D reconstructions, volumetric estimations and geometrical data measurements can be extracted; and undoubtedly they are useful in the diagnosis of organ morphological and functional abnormalities.
It is showed that 3D surface shaded images in angles, around the organ, are sensitive with the possibility to extract quantitative information for all organs that are studied. 3D imaging offers direct visualization and measurements of complex structures of the internal organs of the human body, which cannot be satisfactorily evaluated using 2D imaging.
As reconstruction methods improve in accuracy and in ability to manipulate large matrices, new threshold techniques and corrections will be used, so that perfect absolute quantitative accuracy will be brought closer.
Mangroves act as frontiers that protect the coastal land against destruction of ocean waves, tsunamis and storms. Mangroves also provide habitat for various aquatic life forms and function as natural filter, which improves the quality of water. Mangroves also play important roles as a significant carbon sink in coastal environment. It is interesting fact that despite only 0.05% of plant biomass stored in the ocean and coastal areas out of the total plant biomass on land, it can absorb a comparable amount of carbon every year. A study demonstrated that primary productivity in mangroves is higher than other types of forests. Biomass carbon in mangroves stands is among the highest in the tropics. Mangroves can store up to four times more carbon (C) as compared to other tropical forests around the world [1].
\nA mangroves ecosystem has an ability to absorb carbon dioxide (CO2) and store carbon 40% more than the dry land forest ecosystem. Due to this ability, the total carbon deposited in a square kilometer of mangrove ecosystem is 50 times faster than those of the same area in a dryland tropical forest ecosystem. The absorbed CO2 is stored not only in the plants, but in layers of soils underneath [2]. Therefore, mangroves are playing a crucial role in global carbon budgets and thus mitigating climate change.
\nHowever, despite being realized the importance of mangroves in the global carbon cycle and climate change, the extents of mangroves have inevitably declined since the last few decades. Unfortunately, the declines have been resulting mainly from human activities such as aquaculture expansion, coastal development, and over-harvesting [3]. Malaysia is one of the countries in South East Asia that has among the largest extents of mangroves. Despite its extensive distribution of mangrove ecosystem, this forest is inevitable from threats by various land use activities. The total area of mangrove forest was approximately 2% (650,000 ha) of the total land area in Malaysia in the 1990s [4].
\nHowever, the mangroves in Malaysia have been gradually diminishing, where the total area of mangrove forest has reduced to approximately 580,000 ha in the last decade [5]. Other reports indicated that the extent of mangrove areas in Malaysia is decreasing, from about 700,000 ha in 1975 to 572,000 ha in 2000 due to the intensive harvesting and natural wave actions [6, 7]. Globally, mangroves have also declined from 18.8 million ha to 15.6 million ha between years 1980 and 2005 [8]. Overall Asia was the largest net loss of mangroves since 1980, with about 1.9 million ha have loss, mainly due to conversion of mangrove forest to other land uses. However, there has been a slowdown in the annual rate of mangrove loss, from about 187,000 ha in the 1980s to 102,000 ha between 2000 and 2005. This reflects an increased awareness and an improved management system in mangroves ecosystem.
\nMajor threats towards the mangroves that are triggered by human activities can generalized into six [9], which are (i) conversion to other uses, (ii) overharvesting, (iii) overfishing, (iv) pollution, (v) sedimentation and (vi) alteration of flow regimes. Direct conversion to other uses was identified as the major factor that changes the world’s mangroves. This includes conversions to (i) urban and industrial areas, (ii) aquaculture, and (iii) agriculture. Additionally, natural phenomena such as coastal erosion, storm and lightning strikes are also the natural impacts that kill mangroves in Peninsular Malaysia, including the tragic tsunami on 24 December 2004.
\nDespite widespread concern and numerous case studies describing local issues and challenges, comprehensive information on the global extent of mangroves and trends of deforestation is largely lacking [10]. It is because determining the precise area of mangroves is not always easy. Measurement is affected by varying definitions of what constitutes mangroves; inclusion only on the basis of official recognition such as gazetted forest reserves; scattered or sparse areas considered too inconsequential for inclusion; and the accuracy of the returns made by the responsible authorities. Each of these can create uncertainty and produce significant variation depending on the timing and purpose of the assessment exercise.
\nRecently, RS satellites have been widely used for mangrove monitoring. They greatest reasons why is because the RS can (i) acquire information over large areas, (ii) produce repeated measurement over a place, and (iii) make full use of electromagnetic spectrum for quantitative and qualitative measurements over mangroves [11]. Satellites also provide information on spatial distribution and temporal changes of mangrove forests. When this information is gathered over decades, the mangrove monitoring over the large area will become possible. There are studies on the assessment of mangroves changes and identifying threats, for example in Terengganu [12], Selangor [13], and Peninsular Malaysia [14]. However, these studies are unable to represent the holistic conditions at national level. Therefore, this study was conducted to provide the information pertaining status of mangroves and changes that occurred since the last three decades.
\nThe study area covers the entire mangroves ecosystem in Malaysia, which can be divided into two regions, which are Peninsular Malaysia and East Malaysia (i.e. Malay Borneo). Forests in these regions can be divided into three major types, which are inland dipterocarps (dryland), peat swamp, mangrove forests (wetlands). The mangrove forest is a unique ecosystem and the second largest wetland forest type after the peat swamp forest. Ecologically based on elevation the mangrove forest is located at the lowest elevation, which is equivalent to the sea level. The mangrove forest is generally found along sheltered coasts where it grows abundantly in saline soil and brackish water dominated mainly by trees from the Rhizophoraceae family. Mangroves are fringing the coastlines (up to 5 km landward) and major estuaries of the regions and they reside on wetlands ecosystem of not more than 20 m land altitude.
\nImages from Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper (ETM+), and Landsat-8 Operational Land Imager (OLI) satellite were used in this study. Images from three different epochs, which are 1990, 2000 and 2017 were acquired to conduct the work. For the respective years were utilized in this study. All images are available at
Landsat scenes that were used for the classification. Numbers within the scene boundary indicates path/row ID of Landsat satellites.
Cloud cover is inevitable on the images acquired by the satellites. However, cloud patching process can eliminate the cloud covers that appear on a single-date observation data. Images of particular scenes that were acquired on different dates were used for cloud patching process as shown in Figure 2. F_mask algorithm was used to perform this process [15, 16]. Seamless mosaics product (i.e. images without cloud covers and atmospherically corrected) were used as input for subsequent processes.
\nCloud detection and removal process. Individual Landsat scene that was captured on 26 January 2017 (a) was merged with that captured on 14 June 2017 (b), where both produced a cloud-free images for the year 2017 (c).
Appropriate enhancement techniques were applied to the images to make the mangroves appear better on the images [17]. In addition to the individual spectral bands of Landsat images, vegetation indices such as Normalized Different Vegetation Index (NDVI), Green Atmospherically Resistant Index (GARI), and Normalized Difference Infrared Index (NDII) were also derived from the images to improve quality of classification. The vegetation indices that were used in this study are summarized in Table 1.
\nVegetation indices that were used derived from the images.
Note: NIR = near infrared, G = green, B = blue, R = red, and MidIR = middle wave infrared channels.
Most spectral-based image classifications are performed using traditional methods such as maximum likelihood, linear discriminant analysis, and spectral angle mapper classifiers. These methods are applied to the spectral bands to produce a classified feature in images [18]. Instead of using these approaches, this study attempted a new approach to classify the images. R Package, which is free, open source software with the RandomForest algorithm [19] was used.
\nRandomForest implements Breiman’s RandomForest algorithm, based on Breiman and Cutler’s original FORTRAN code for classification and regression [20]. It can also be used for assessing proximities among data points without necessarily a training set. All sampling points that were collected on the ground were connected to the corresponding pixels on the image through this algorithm. Classification was done by searching the most important variables i.e. which spectral bands are used in decision tree approach [21, 22, 23]. RandomForest applies four major steps of looking at the importance of variables as follow:
Step 1: to determine the significance of the mth variable. In the left out cases for the kth tree, randomly permute all values of the mth variable. Put these new covariate values down the tree and get classifications.
Steps 2 and 3: for the nth case in the data, its margin at the end of a run is the proportion of votes for its true class minus the maximum of the proportion of votes for each of the other classes. The 2nd measure of importance of the mth variable is the average lowering of the margin across all cases when the mth variable is randomly permuted as in Step 1. Step 3 then count the margins that was shrank.
Step 4: the splitting criterion used in RandomForest is the Gini criterion, a mechanism that can measure the most to least importance of variables used in decision tree. At every split, one of the mth variables is used to form the split and there is a resulting decrease in the Gini. The sum of all will decrease the forest due to a given variable, normalized by the number of trees.
All images have been classified to distinguish mangroves from the other land uses. The classification results were transformed into vector shapefile for further refinement and editing. The accuracy of the classification results were assessed by using a number of ground truth points. The GIS platform was used to carry out post-classification analysis. Post-classification analysis is usually used for quantifying changes of land uses. Changes of mangroves were identified from the conversions of mangroves to other landuse classes, which are (i) urban, settlement, and industrial areas, (ii) agricultural, (iii) aquaculture activities, and (iv) coastal erosion.
\nCarbon dioxide (CO2) is defined as natural, colorless and odorless greenhouse gas that is emitted when fossil fuels (i.e. natural gas, oil, coal, etc.) are burnt. In this study, the CO2 emission is expressed as C loss, assuming that the gas is emitted when deforestation occur. The units of metric tons C was converted to CO2 by multiplying the ratio of the molecular weight of carbon dioxide to that of carbon (44/12 = 3.67) [24].
\nThe CO2 resulted from deforestation is one of the important elements in greenhouse gases emissions. Therefore, it is also essential to quantify the contribution of mangrove deforestation towards the CO2 emission. Net emission as resulted from deforestation of mangroves can be estimated based stock-difference method, which can be expressed as Eq. (1) as follow [24];
\nwhere ∆C is changes in carbon stock (Mg C yr−1), Ct1 and Ct2 (Mg C) is carbon stock at time t1 and t2 (year), respectively. In this case, the Ct1 and Ct2 was quantified from the changes analysis that have been carried out earlier this study.
\nF mask algorithm successfully removed almost 100% of cloud covers and their shadows on the images. The algorithm also managed to detect thin, low temperature clouds in the high altitude by thermal sensors onboard the Landsat TM, ETM+ and OLI. The algorithm somehow failed to detect small scattering clouds that occurred in small patches on the images. Nevertheless, the algorithm has facilitated the cloud removal process and make the mangroves mapping and monitoring work at landscape-level practical. Figure 2 shows a portion of mangroves on two different images that were captured on different dates with clouds. These images were used to produce seamless mosaic of images without cloud covers.
\nThe study indicated that the suitable spectral bands for species discrimination varied with scale. However, near-infrared (700–1327 nm) bands were consistently important spectrum across all scales and the visible bands (437–700 nm) were more important at pixel and crown scales. By using the RandomForest algorithm, the most important bands in the classification were represented by a mean decrease Gini values. The most important bands in mangroves discrimination, from most to least, are; MidIR, NIR-2, NIR, Green, Blue, Red. Spectral profile of the images also showed that the NIR channels separate the mangroves from the other land covers very well (Figure 3). On the other hands, the vegetation indices that were used in this study played similar important role in mangroves classification.
\nSpectral profiles of several land covers extracted from the images. Channel 1 through 6 on the y-axis are blue, green, red, NIR, NIR-2 and MidIR, respectively.
The image classification approach that has been applied in this study was found to be effective only at large coverage of mangroves. The accuracy for all classifications were ranging from 83 to 91%, which were acceptable and reliable for monitoring purpose. Mangroves are normally appear dark on any combination of spectral bands of multispectral image. This is due to the natural ecosystem of mangroves, which is covered by swamps and sometimes inundated by tidal water. The chlorophyll content of the mangrove leaves, which is higher than those of trees and crops, tends to make them appear darker on satellite images [25], as depicted in Figure 4. Each mangrove species has a unique configuration of trunks, prop roots and pneumatophores that works as a different drag force therefore resulting in a different reduction rate of sea waves (Figure 5). Not only this, the wet floor of the forest gives special spectral characteristics on satellites images that can be differentiated easily from other features (Figure 6).
\nImages showing (a) combination of bands 5, 6 and 4 of Landsat-8 OLI and (b) combination of vegetation indices, NDVI, GARI and NDII. These images were selected for the classification process.
Roots and successive stands of Rhizophora apiculata in a common mature mangrove forest.
Mangroves as they appeared on Landsat-8 image. The dark green areas represent the mangrove areas. The image classification process, either automated or manual digitizing, is usually easier for mangrove areas than for other vegetation. The image is displayed using a combination of bands 543 (RGB) over the Kapar area in Klang, Selangor. The central bottom is Klang port complex and the bottom left is Pulau Klang, which is predominantly covered by mangroves.
The classification results were further edited to refine the shapes and accuracy. This process was conducted manually on the vector shapefile by visual interpretation on GIS platform. Finally the spatial distribution of the mangroves were mapped properly (Figure 7). The mangroves in Malaysia were mostly found in Sabah (60%), followed by Sarawak (22%) and Peninsular Malaysia (18%). Table 2 summarizes the total extents of mangroves in the respective regions that have been produced from the classification. It is notable that the total extents of mangroves have been decreasing throughout the monitoring period. Figure 8 shows spatially explicit map of mangroves distribution in Malaysia as of year 2017. Mangroves are found mainly along the west coast of Peninsular Malaysia, west coast of Sarawak and the east coast of Sabah.
\nMangroves appear dark green on the original image (left) and the classified mangroves, indicated as red polygons (right).
Region | \nMangroves 1990 (ha) | \nMangroves 2000 (ha) | \nMangroves 2017 (ha) | \n
---|---|---|---|
Peninsular Malaysia | \n116,746 | \n114,353 | \n110,953 | \n
Sabah | \n385,630 | \n382,448 | \n378,195 | \n
Sarawak | \n147,936 | \n145,263 | \n139,890 | \n
Total | \n650,311 | \n642,063 | \n629,038 | \n
Extents of mangroves in Malaysia.
Distribution of mangroves in Malaysia over the year 2017.
Table 3 reports the changes of mangroves that occurred over the 27 years of monitoring period. The total loss of mangroves was about 21,274 ha where majority of the mangroves loss were outside the Permanent Forest Reserve or within the stateland areas. These areas are actually the land bank for the states developments, which are principally included in the State’s Structural Planning. Example of mangroves changes detected from the multi-temporal mapping process is shown in Figure 9. From this information, it can be concluded that the annual decrease rate of mangroves was about 788 ha per year or about 0.13% per annum since year 1990. Major factors that contributed to these changes have been identified as: (i) direct conversion to other land uses (Figure 10), predominantly for commercial-scale agriculture (Figure 11) and aquaculture (Figure 12), and (ii) coastal erosion (Figure 13). The other factors such as overharvesting and pollution affect the mangroves to a lesser degree.
\nRegion | \nMangrove loss 1990–2000 (ha) | \nMangrove loss 2000–2017 (ha) | \nRate of deforestation 1990–2000 (ha yr−1) | (% yr−1) | \nRate of deforestation 2000–2017 (ha yr−1) | (% yr−1) | \nAverage rate of deforestation 1990–2017 (ha yr−1) | (% yr−1) | \n
---|---|---|---|---|---|
Peninsular Malaysia | \n2393 | \n3400 | \n239 | 0.20 | \n200 | 0.17 | \n215 | 0.19 | \n
Sabah | \n3182 | \n4253 | \n318 | 0.08 | \n250 | 0.07 | \n275 | 0.07 | \n
Sarawak | \n2673 | \n5373 | \n267 | 0.18 | \n316 | 0.22 | \n298 | 0.21 | \n
Total | \n8227 | \n13,190 | \n823 | 0.13 | \n776 | 0.12 | \n793 | 0.13 | \n
Mangroves deforestation in Malaysia between years 1990 and 2017.
Changes of mangroves that occurred between 1990 and 2017 overlaid on GIS platform.
Land developments on mangroves. Reddish color represents newly opened areas for development purposes that were cleared from the original mangroves areas (dark green color).
Expansion of oil palm plantation on mangroves. Reddish color represents newly opened plantations from the original mangroves areas (dark green color). The bright green represents existing plantations.
Expansion of aquaculture industries on mangroves. Dark blue patches represents newly opened aquaculture ponds from the original mangroves areas (dark green color).
Shoreline changes that resulted from coastal erosion along the coast of south Pontian, Johor. The study indicated that 14.2 km stretches have been facing serious coastal erosion within the last two decades with the rate of erosion ranging from 3.2 to 12.5 m per year.
Although coastal erosion was identified as one of the factors of mangroves loss, there were some accretions occurred in some other places. Erosion and accretion is a dynamic process and takes place along the coastlines and major estuaries, where suspended sediments are likely to settle. These phenomena also lead to species succession when the existing plant species die due to unsuitable soil and new species emerge. Besides, mangrove roots can act as wave breaker and promote flocculation and sedimentation, eventually forming mudflats that allow positive accretion (Figure 14). Coastal erosion occurs when the waves hit perpendicular to the coastlines and when the rapid flow of sea currents wash away the sand or soil particles. The frequency and height of waves hitting the coastlines contribute to the harshness of coastal erosion. Thus, the presence of mangroves can reduce the coastal erosion significantly. This condition is obvious particularly in the areas facing the sea [26, 27].
\nPositive accretion of mangroves at estuaries. The new formations at the river mouths were colonized by mangroves trees forming a naturally generated forest.
A study has indicated that the average C stock (aboveground and belowground) in mangroves in Malaysia is about 181 Mg C ha−1 [28]. The extents of mangroves loss for each epoch were multiplied by this average carbon stocks. The study demonstrated that the total loss of carbon due to the loss of mangroves was about 2.6 million Mg C. Subsequently, this has led to the CO2 emission at about 14.2 million Mg CO2, with an average of about 0.5 million Mg CO2 emission per year, along the monitoring period. Table 4 summarizes the impact of mangroves loss in terms of CO2 emission. Although the figures are generally crude, the study provided some ideas for further studies, especially which related to carbon cycles and climate change.
\nRegion | \nMangrove loss (ha) | \nCarbon loss (Mg C) | \nCO2 emission (Mg CO2) | \nRate of CO2 emission (Mg CO2 yr−1) | \n
---|---|---|---|---|
Peninsular Malaysia | \n5793 | \n1,048,567 | \n3,848,242 | \n142,527 | \n
Sabah | \n7435 | \n1,345,672 | \n4,938,617 | \n182,912 | \n
Sarawak | \n8046 | \n1,456,288 | \n5,344,578 | \n197,947 | \n
Total | \n21,417 | \n3,876,409 | \n14,226,422 | \n526,905 | \n
CO2 emission resulted from mangroves loss between years 1990 and 2017.
This study has successfully assessed the current state of mangroves and determined the rate of mangroves loss in Malaysia since the last decade. Total mangroves in Malaysia has decreased from 650,311 ha in 1990 to 629,038 ha in 2017. Total deforestation was accounted at 21,274 ha or 3.3% with the annual rate of deforestation of 788 ha yr−1 or 0.13% yr−1, between 1990 and 2017. The study also quantified the C stock changes and estimated CO2 emission due to the loss of mangroves in Malaysia. Total emission caused by the mangroves deforestation was accounted at about 14 million Mg CO2 with annual emission rate of around 0.5 million Mg CO2 yr−1.
\nThe study found that the Landsat-based mapping and monitoring of mangroves was very practical. It provides a reliable information on mangroves distribution, both qualitatively and quantitatively. Landsat missions provide a very useful RS tool for monitoring changes of mangroves over time. The study suggests that appropriate actions should be taken by the Government of Malaysia to protect the mangroves and keep their ecosystem intact forever. The most effective way to conserve the mangroves is to gazette the remaining stateland forest as Permanent Reserved Forests (PRFs). These PRFs should then be maintained as amenity for current and future generations, while contributing to the mitigation of climate change impacts at the local level. Any development in PRFs should be prohibited or implemented with caution.
\nOverall, there is great potential in the application of Landsat-based data with appropriate GIS technique for mapping and monitoring of mangroves in Malaysia. Although there are cloud covers problems on some of the images, this has not hindered the assessment of mangroves at landscape and regional levels. The accuracy and precision also vary depending on the objective of the application. However, the ability to detect major changes in the ecosystem that can cause profound and irreversible damage far outweighs a perfectly or highly accurate and precise RS based method at this point.
\nCurrently, Malaysia has reserved about 85% (~535,000 ha) out of the total areas of mangroves as Permanent Forest Reserve and State/National Parks. The remaining 15% is under the state-lands and alienated lands. By far, the most effective way to preserve these mangroves is through gazzeting into permanent forest reserves.
\nThis work has been carried out under the Research and Development Committee on Mangroves (JTRD) led by FRIM. Special thanks for the Forestry Department Peninsular Malaysia (JPSM), Sabah Forestry Department (SFD), and Forest Department Sarawak (FDS) for the supports on the ground data collection activities.
\nThe authors declare no ‘conflict of interest’ for this chapter.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
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\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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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. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. 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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