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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

Our goal is to develop automated methods for the segmentation of thr- dimensional biomedical images. Here, we describe the segmentation of c- focal microscopy images of bee brains (20 individuals) by registration to one or several atlas images. Registration is performed by a highly parallel imp- mentation of an entropy-based nonrigid registration algorithm using B-spline transformations. We present and evaluate different methods to solve the cor- spondence problem in atlas based registration. An image can be segmented by registering it to an individual atlas, an average atlas, or multiple atlases. When registering to multiple atlases, combining the individual segmentations into a?nalsegmentationcanbeachievedbyatlasselection,ormulticlassi?erdecision fusion. Wedescribeallthesemethodsandevaluatethesegmentationaccuracies that they achieve by performing experiments with electronic phantoms as well as by comparing their outputs to a manual gold standard. The present work is focused on the mathematical and computational t- ory behind a technique for deformable image registration termed Hyperelastic Warping, and demonstration of the technique via applications in image regist- tion and strain measurement. The approach combines well-established prin- ples of nonlinear continuum mechanics with forces derived directly from thr- dimensional image data to achieve registration. The general approach does not require the de?nition of landmarks,?ducials, or surfaces, although it can - commodate these if available. Representative problems demonstrate the robust and?exible nature of the approach. Three-dimensional registration methods are introduced for registering MRI volumes of the pelvis and prostate. The chapter?rst reviews the applications, xi xii Preface challenges,and previous methods of image registration in the prostate.

Handbook of Biomedical Image Analysis: Segmentation Models (Volume II) is dedicated to the segmentation of complex shapes from the field of imaging sciences using different mathematicaltechniques. 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: --- Model-based brain tissue classification --- Supervised texture classification for intravascular tissue characterization --- Medical image segmentation: methods and applications in functional imaging --- Automatic segmentation of pancreatic tumors in CT --- Computerized analysis and vasodilation parameterization in flow-mediated dilation tests from ultrasonic image sequences --- Adaptive approaches to optical segmentation in brain MRI Images --- Automatic analysis of color fundus photographs and its application to the diagnosis of diabetic retinopathy --- Segmentation issues of carotid plaque analysis in MRI --- Accurate lumen identification, detection, and quantification in MR plaque volumes --- Hessian-based multiscale enhancement, description, and quantification of second-order 3D local structures from medical volume data --- A knowledge-based scheme for digital mammography --- Simultaneous fuzzy segmentation --- CAD of mammographic calicification: impact of segmentation About the Editors: Jasjit Suri, Ph.D. has spent over 20 years in the field of computer and electrical engineering, and more than a decade in imaging sciences. Dr. Suri has a masters degree in computer sciences from the University of Illinois and a doctorate in Electrical Engineering from the University of Washington, Seattle. Dr. Suri has published over 125 technical publications in medical imaging, as well as being a senior member of IEEE and a member of the engineering honor societies Eta-Kappa-Nu and Tau-Beta-Phi, and a recipient of the President's Gold Medal in 1980. He is also a fellow of American Institute of Medical and Biological Engineering. David Wilson, Ph D. is a Professor of Biomedical Engineering and Radiology at Case Western Reserve University, having gained his doctorate from Rice University. He has over 60 refereed journal publications and is co-owner of several patents. Professor Wilson has actively developed biomedical imaging at CWRU. He has led a faculty recruitment effort, and he has served as PI or has been an active leader on multiple research and equipment developmental awards given to CWRU, including an NIH planning grant award for an in vivo Cellular and Molecular Imaging Center and an Ohio Wright Center of Innovation award. Swamy Laxminarayan, Dsc championed the field of Biomedical Engineering for over 30 years, having held a variety of senior positions within the industry. He is an internationally recognized scientist, engineer, and educator and has been published in over 200 technical publications in biomedical information technology, computation biology, signal and image processing, biotechnology, and physiological system modeling. Prof. Laxminarayan is a fellow of AIMBE and a recipient of IEEE 3rd Millennium Medal.