Handbook of Biomedical Image Analysis: Segmentation Models (Volume I) is dedicated to the segmentation of complex shapes from the field of imaging sciences using different mathematical techniques. This volume is aimed at researchers and educators in imaging sciences, radiological imaging, clinical and diagnostic imaging, physicists covering different medical imaging modalities, as well as researchers in biomedical engineering, applied mathematics, algorithmic development, computer vision, signal processing, computer graphics and multimedia in general, both in academia and industry . Key Features: - Principles of intra-vascular ultrasound (IVUS) - Principles of positron emission tomography (PET) - Physical principles of magnetic resonance angiography (MRA). - Basic and advanced level set methods - Shape for shading method for medical image analysis - Wavelet transforms and other multi-scale analysis functions - Three dimensional deformable surfaces - Level Set application for CT lungs, brain MRI and MRA volume segmentation - Segmentation of incomplete tomographic medical data sets - Subjective level sets for missing boundaries for segmentation
This publication starts of with a review of plaque imaging techniques, with an introduction of the segmentation techniques for plaque classification and quantification. Many aspects of plaque imaging techniques are presented in this publication, such as; medical image retrieval and database management, MRI techniques to differentiate stable versus high risk atherosclerosis, composition and morphology of atherosclerotic plaque, analysis of the soft tissue based on computer vision techniques, modelling of coronary artery biomechanics, Cardiac CT for the assessment of cardiovascular pathology with an emphasis on the detection of coronary atherosclerosis, technical and practical issues regarding coronary atherosclerotic plaque imaging by CT (focussing on coronary calcium imaging), feasibility of a non-invasive, in vivo determination of the IBS of arterial wall tissue, high resolution ultrasound images of carotid plaques, the problem of reliable features extraction and classification process and a discussion on advanced mathematical techniques to extract spectral information from the RF data to determine the plaque composition.