Part of the book: Biomedical Engineering
Detection of regional ventricular dysfunction is a challenging problem. This study presents an efficient method based on ultrasound (US) imaging and finite element (FE) analysis, for detecting akinetic and dyskinetic regions in the left ventricle (LV). The underlying hypothesis is that the contraction of a healthy LV is approximately homogeneous. Therefore, any deviations between the image-based measured deformation and a homogeneous contraction FE model should correspond to a pathological region. The method was first successfully applied to synthetic data simulating an acute ischemia; it demonstrated that the pathological areas were revealed with a higher contrast than those observed directly in the deformation maps. The technique was then applied to a cohort of eight left bundle branch block (LBBB) patients. For this group, the heterogeneities were significantly less pronounced than those revealed for the synthetic cases but the method was still able to identify the abnormal regions of the LV. This study indicated the potential clinical utility of the method by its simplicity in a patient-specific context and its ability to quickly identify various heterogeneities in LV function. Further studies are required to determine the model accuracy in other pathologies and to investigate its robustness to noise and image artifacts.
Part of the book: Medical and Biological Image Analysis
Fourier filtering for image denoising consists in masking parts of the Fourier spectrum of an image and using inverse Fourier transform of the masked image to obtain the denoised one. In cases of directional noise, this process can induce artifacts, mainly because of the spatial coherence that exists in the theoretical noise-free image. Moreover, it can lead to loss of low-frequency content that is important in applications such as fringe projection technique, which aims at measuring 3D elevations of a surface. A method based on the principle of Fourier spectrum cloning for the denoising of images is proposed in this chapter. This method improves the PSNR and the SSIM ratio in comparison with spectrum masking denoising. The method will be detailed first, and then examples of image denoising in two different applications will be presented.
Part of the book: Fourier Transforms