Table of Contents

Fundamental Enhancement Techniques; Adaptive Image Filtering; Enhancement by Multiscale Nonlinear Operators; Medical Image Enhancement with Hybrid Filters; Overview and Fundamentals of Medical Image Segmentation; Image Segmentation by Fuzzy Clustering: Methods and Issues; Segmentation with Neural Networks; Deformable Models; Shape Information in Deformable Models; Gradient Vector Flow Deformable Models; Fully Automated Hybrid Segmentation of the Brain; Unsupervised Tissue Classification; Partial Volume Segmentation with Voxel Histograms; Higher Order Statistics for Tissue Segmentation; Two-dimensional Shape and Texture Quantification; Texture Analysis in Three Dimensions for Tissue Characterization; Computational Neuroanatomy Using Shape Transformations; Tumor Growth Modeling in Oncological Image Analysis; Arterial Tree Morphometry; Image-Based Computational Biomechanics of the Musculoskeletal System; Three-Dimensional Bone Angle Quantification; Database Selection and Feature Extraction for Neural Networks; Quantitative Image Analysis for Estimation of Breast Cancer Risk; Classification of Breast Lesions in Mammograms; Quantitative Analysis of Cardiac Function; Image Processing and Analysis in Tagged Cardiac MRI; Analysis of Cell Nuclear Features in Fluorescence Microscopy Images; Image Interpolation and Resampling; Physical Basis of Spatial Distortions in Magnetic Resonance Images; Physical and Biological Bases of Spatial Distortions in PET Images; Biological Underpinnings of Anatomic Consistency and Variability in the Human Brain; Spatial Transformation Models; Validation of Registration Accuracy; Landmark-based Registration Using Features Identified through Differential Geometry; Image Registration Using Chamfer Matching; Within-Modality Registration Using Intensity-Based Cost Functions; Across-Modality Registration Using Intensity-Based Cost Functions; Talairach Space as a Tool for Intersubject Standardization in the Brain; Warping Strategies for Intersubject Registration; Optimizing the Resampling of Registered Images; Clinical Applications of Image Registration; Registration for Image-Guided Surgery; Image Registration and the Construction of Multidimensional Brain Atlases; Visualization Pathways in Biomedicine; Three-Dimensional Visualization in Medicine and Biology; Volume Visualization in Medicine; Fast Isosurface Extraction Methods for Large Image Data Sets; Computer Processing Methods for Virtual Endoscopy; Fundamentals and Standards of Compression and Communication; Medical Image Archive and Retrieval; Image Standardization in PACS; Imaging and Communication in Medical and Public Health Informatics; Dynamic Mammogram Retrieval from Web-Based Image Libraries; Quality Evaluation for Compressed Medical Images: Fundamentals; Quality Evaluation for Compressed Medical Images: Diagnostic Accuracy; Quality Evaluation for Compressed Medical Images: Statistical Issues; Three-Dimensional Image Compression with Wavelet Transforms. The Handbook of Medical Image Processing and Analysis is a comprehensive compilation of concepts and techniques used for processing and analyzing medical images after they have been generated or digitized. The Handbook is organized into six sections that relate to the main functions: enhancement, segmentation, quantification, registration, visualization, and compression, storage and communication. The second edition is extensively revised and updated throughout, reflecting new technology and research, and includes new chapters on: higher order statistics for tissue segmentation; tumor growth modeling in oncological image analysis; analysis of cell nuclear features in fluorescence microscopy images; imaging and communication in medical and public health informatics; and dynamic mammogram retrieval from web-based image libraries. For those looking to explore advanced concepts and access essential information, this second edition of Handbook of Medical Image Processing and Analysis is an invaluable resource. It remains the most complete single volume reference for biomedical engineers, researchers, professionals and those working in medical imaging and medical image processing. Dr. Isaac N. Bankman is the supervisor of a group that specializes on imaging, laser and sensor systems, modeling, algorithms and testing at the Johns Hopkins University Applied Physics Laboratory. He received his BSc degree in Electrical Engineering from Bogazici University, Turkey, in 1977, the MSc degree in Electronics from University of Wales, Britain, in 1979, and a PhD in Biomedical Engineering from the Israel Institute of Technology, Israel, in 1985. He is a member of SPIE. * Includes contributions from internationally renowned authors from leading institutions * NEW! 35 of 56 chapters have been revised and updated. Additionally, five new chapters have been added on important topics incluling Nonlinear 3D Boundary Detection, Adaptive Algorithms for Cancer Cytological Diagnosis, Dynamic Mammogram Retrieval from Web-Based Image Libraries, Imaging and Communication in Health Informatics and Tumor Growth Modeling in Oncological Image Analysis. * Provides a complete collection of algorithms in computer processing of medical images * Contains over 60 pages of stunning, four-color images.