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.

Key Features

* 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


Biomedical engineers, doctors, biological reseachers, medical informatics professionals, radiologists, image processors

Table of Contents

ENHANCEMENT Introduction 1. Fundamental Enhancement Techniques 2. Adaptive Image Filtering 3. Enhancement by Multiscale Nonlinear Operators 4. Medical Image Enhancement with Hybrid Filters SEGMENTATION Introduction 5. Overview and Fundamentals of Medical Image Segmentation 6. Image Segmentation by Fuzzy Clustering: Methods and Issues 7. Segmentation with Neural Networks 8. Deformable Models 9. Shape Information in Deformable Models 10. Gradient Vector Flow Deformable Models 11. Fully Automated Hybrid Segmentation of the Brain 12. Unsupervised Tissue Classification 13. Partial Volume Segmentation with Voxel Histograms 14. Higher Order Statistics for Tissue Segmentation QUANTIFICATION Introduction 15. Two-dimensional Shape and Texture Quantification 16. Texture Analysis in Three Dimensions for Tissue Characterization 17. Computational Neuroanatomy Using Shape Transformations 18. Tumor Growth Modeling in Oncological Image Analysis 19. Arterial Tree Morphometry 20. Image-Based Computational Biomechanics of the Musculoskeletal System 21. Three-Dimensional Bone Angle Quantification 22. Database Selection and Feature Extraction for Neural Networks 23. Quantitative Image Analysis for Estimation of Breast Cancer Risk 24. Classification of Breast Lesions in Mammograms 25. Quantitative Analysis of Cardiac Function 26. Image Processing and Analysis in Tagged Cardiac MRI 27. Analysis of Cell Nuclear Features in Fluorescence Microscopy Images 28. Image Interpolation and Resampling


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© 2009
Academic Press
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About the author

Isaac Bankman

Isaac Bankman is affiliated with Johns Hopkins University