Handbook of Medical Image Processing and Analysis.
Title:
Handbook of Medical Image Processing and Analysis.
Author:
Bankman, Isaac.
ISBN:
9780080559148
Personal Author:
Edition:
2nd ed.
Physical Description:
1 online resource (1009 pages)
Contents:
Front Cover -- Handbook of Medical Image Processing and Analysis -- Copyright Page -- Contents -- Foreword -- Contributors -- Preface -- Acknowledgments -- Part I Enhancement -- Chapter 1 Fundamental Enhancement Techniques -- 1.1 Introduction -- 1.2 Preliminaries and Definitions -- 1.3 Pixel Operations -- 1.4 Local Operators -- 1.5 Operations with Multiple Images -- 1.6 Frequency Domain Techniques -- 1.7 Concluding Remarks -- 1.8 References -- Chapter 2 Adaptive Image Filtering -- 2.1 Introduction -- 2.2 Multidimensional Spatial Frequencies and Filtering -- 2.3 Random Fields and Wiener Filtering -- 2.4 Adaptive Wiener Filters -- 2.5 Anisotropic Adaptive Filtering -- 2.6 References -- Chapter 3 Enhancement by Multiscale Nonlinear Operators -- 3.1 Introduction -- 3.2 One-Dimensional Discrete Dyadic Wavelet Transform -- 3.3 Linear Enhancement and Unsharp Masking -- 3.4 Nonlinear Enhancement by Functional Mapping -- 3.5 A Method for Combined Denoising and Enhancement -- 3.6 Two-Dimensional Extension -- 3.7 Experimental Results and Comparison -- 3.8 Conclusion -- 3.9 References -- Chapter 4 Medical Image Enhancement Using Fourier Descriptors and Hybrid Filters -- 4.1 Introduction -- 4.2 Design of the Hybrid Filter -- 4.3 Experimental Results -- 4.4 Discussion and Conclusion -- 4.5 References -- Part II Segmentation -- Chapter 5 Overview and Fundamentals of Medical Image Segmentation -- 5.1 Introduction -- 5.2 Thresholding -- 5.3 Region Growing -- 5.4 Watershed Algorithm -- 5.5 Edge-Based Segmentation Techniques -- 5.6 Multispectral Techniques -- 5.7 Other Techniques -- 5.8 Concluding Remarks -- 5.9 References -- Chapter 6 Image Segmentation by Fuzzy Clustering: Methods and Issues -- 6.1 Introduction -- 6.2 The Quantitative Basis of Fuzzy Image Segmentation -- 6.3 Qualitative Discussion of a Few Fuzzy Image Segmentation Methods.
6.4 Conclusions and Discussion -- 6.5 References -- Chapter 7 Segmentation with Neural Networks -- 7.1 Introduction -- 7.2 Structure and Function of the GRBF Network -- 7.3 Training Procedure -- 7.4 Application to Medical Image Segmentation -- 7.5 Image Data -- 7.6 Preprocessing -- 7.7 Vector Quantization -- 7.8 Classification -- 7.9 Results -- 7.10 Discussion -- 7.11 Topical Applications, Conceptual Extensions, and Outlook -- 7.12 Conclusion and Outlook -- 7.13 References -- Chapter 8 Deformable Models -- 8.1 Introduction -- 8.2 Mathematical Foundations of Deformable Models -- 8.3 Medical Image Analysis with Deformable Models -- 8.4 Discussion -- 8.5 Conclusion -- 8.6 References -- Chapter 9 Shape Information in Deformable Models -- 9.1 Background -- 9.2 Global Shape Constraints -- 9.3 Level Set Methods Incorporating Generic Constraints -- 9.4 Conclusions -- 9.5 References -- Chapter 10 Gradient Vector Flow Deformable Models -- 10.1 Introduction -- 10.2 Background -- 10.3 GVF Deformable Contours -- 10.4 Experiments -- 10.5 3D GVF Deformable Models and Results -- 10.6 Discussion -- 10.7 Conclusions -- 10.8 References -- Chapter 11 Fully Automated Hybrid Segmentation of the Brain -- 11.1 Introduction -- 11.2 Brain Segmentation Method -- 11.3 Other Brain Segmentation Techniques -- 11.4 Summary -- 11.5 References -- Chapter 12 Unsupervised Tissue Classification -- 12.1 Introduction -- 12.2 Background -- 12.3 Methods -- 12.4 Results -- 12.5 Conclusions -- 12.6 References -- Chapter 13 Partial Volume Segmentation and Boundary Distance Estimation with Voxel Histograms -- 13.1 Introduction -- 13.2 Overview -- 13.3 Normalized Histograms -- 13.4 Histogram Basis Functions for Pure Materials and Mixtures -- 13.5 Estimating Histogram Basis Function Parameters -- 13.6 Classification -- 13.7 Results -- 13.8 Derivation of Histogram Basis Functions.
13.9 Derivation of Classification Parameter Estimation -- 13.10 Discussion -- 13.11 Conclusions -- 13.12 References -- Chapter 14 High Order Statistics for Tissue Segmentation -- 14.1 Introduction -- 14.2 Requirements for Using 3rd and 4th Order Statistics -- 14.3 3D Non-linear Edge Detectors -- 14.4 Experiments with Real Data -- 14.5 Discussion and Conclusions -- 14.6 References -- Part III Quantification -- Chapter 15 Two-Dimensional Shape and Texture Quantification -- 15.1 Shape Quantification -- 15.2 Texture Quantification -- 15.3 References -- Chapter 16 Texture Analysis in 3D for Tissue Characterization -- 16.1 Introduction -- 16.2 Issues Related to 3D Texture Estimation and Representation -- 16.3 3D Texture Representation -- 16.4 Feature Extraction -- 16.5 Simulated Data Studies -- 16.6 Applications to Real Data -- 16.7 Conclusions -- 16.8 References -- Chapter 17 Computational Neuroanatomy Using Shape Transformations -- 17.1 Quantifying Anatomy via Shape Transformations -- 17.2 The Shape Transformation -- 17.3 Measurements Based on the Shape Transformation -- 17.4 Spatial Normalization of Image Data -- 17.5 Conclusion -- 17.6 References -- Chapter 18 Tumor Growth Modeling in Oncological Image Analysis -- 18.1 Introduction -- 18.2 Mathematical Models -- 18.3 Image-Guided Tools for Therapy Planning -- 18.4 Applications to Registration and Segmentation -- 18.5 Perspectives and Challenges -- 18.6 References -- Chapter 19 Arterial Tree Morphometry -- 19.1 Introduction -- 19.2 Data Acquisition for Vascular Morphometry -- 19.3 Image Processing for Arterial Tree Morphometry -- 19.4 Arterial Tree Morphometry in Pulmonary Hypertension Research -- 19.5 Discussion and Conclusions -- 19.6 References -- Chapter 20 Image-Based Computational Biomechanics of the Musculoskeletal System -- 20.1 Introduction.
20.2 Three-Dimensional Biomechanical Models of the Musculoskeletal System -- 20.3 Bone Structure and Material Property Analysis -- 20.4 Applications -- 20.5 Summary -- 20.6 References -- Chapter 21 Three-Dimensional Bone Angle Quantification -- 21.1 Introduction -- 21.2 3D Angle Measurement Method -- 21.3 Results -- 21.4 Discussion -- 21.5 References -- Chapter 22 Database Selection and Feature Extraction for Neural Networks -- 22.1 Introduction -- 22.2 Database Selection -- 22.3 Feature Selection -- 22.4 Summary -- 22.5 References -- Chapter 23 Quantitative Image Analysis for Estimation of Breast Cancer Risk -- 23.1 Introduction -- 23.2 Methods for Characterizing Mammographic Density -- 23.3 Planimetry -- 23.4 Semiautomated Feature: Interactive Thresholding -- 23.5 Automated Analysis of Mammographic Densities -- 23.6 Symmetry of Projection in the Quantitative Analysis of Mammographic Images -- 23.7 Variation of Thickness across the Breast: Effect on Density Analysis -- 23.8 Volumetric Analysis of Mammographic Density -- 23.9 Other Imaging Modalities -- 23.10 Applications of Mammographic Density Measurements -- 23.11 References -- Chapter 24 Classification of Breast Lesions from Mammograms -- 24.1 Techniques for Classifying Breast Lesions -- 24.2 Performance of Computer Classification -- 24.3 Effect of Computer Classification on Radiologists' Diagnostic Performance -- 24.4 Methods for Presenting Computer Analysis to Radiologists -- 24.5 Summary -- 24.6 References -- Chapter 25 Quantitative Analysis of Cardiac Function -- 25.1 Dynamic Image Acquisition Techniques -- 25.2 Dynamic Analysis of Left Ventricular Function -- 25.3 Quantitative Evaluation of Flow Motion -- 25.4 Conclusion -- 25.5 References -- Chapter 26 Image Processing and Analysis in Tagged Cardiac MRI -- 26.1 Introduction -- 26.2 Background.
26.3 Feature Tracking Techniques in MR Tagging -- 26.4 Direct Encoding Methods -- 26.5 3-D Motion Estimation -- 26.6 Discussion -- 26.7 References -- Chapter 27 Cytometric Features of Fluorescently Labeled Nuclei for Cell Classification -- 27.1 Introduction -- 27.2 Nuclear Features -- 27.3 Classification Process -- 27.4 Example of Feature Analysis for Classification -- 27.5 Conclusion -- 27.6 References -- Chapter 28 Image Interpolation and Resampling -- 28.1 Introduction -- 28.2 Classical Interpolation -- 28.3 Generalized Interpolation -- 28.4 Terminology and Other Pitfalls -- 28.5 Artifacts -- 28.6 Desirable Properties -- 28.7 Approximation Theory -- 28.8 Specific Examples -- 28.9 Cost-Performance Analysis -- 28.10 Experiments -- 28.11 Conclusion -- 28.12 References -- Part IV Registration -- Chapter 29 The Physical Basis of Spatial Distortions in Magnetic Resonance Images -- 29.1 Introduction -- 29.2 Review of Image Formation -- 29.3 Hardware Imperfections -- 29.4 Effects of Motion -- 29.5 Chemical Shift Effects -- 29.6 Imperfect MRI Pulse Sequences -- 29.7 fMRI Artifacts -- 29.8 Concluding Remarks -- 29.9 References -- Chapter 30 Physical and Biological Bases of Spatial Distortions in Positron Emission Tomography Images -- 30.1 Introduction -- 30.2 Physical Distortions of PET -- 30.3 Anatomical Distortions -- 30.4 Methods of Correction -- 30.5 Summary -- 30.6 References -- Chapter 31 Biological Underpinnings of Anatomic Consistency and Variability in the Human Brain -- 31.1 Introduction -- 31.2 Cerebral Anatomy at the Macroscopic Level -- 31.3 Cerebral Anatomical Variability -- 31.4 Anatomical Variability and Functional Areas -- 31.5 Conclusion -- 31.6 References -- Chapter 32 Spatial Transformation Models -- 32.1 Homogeneous Coordinates -- 32.2 Rigid-Body Model -- 32.3 Global Rescaling Transformation -- 32.4 Nine-Parameter Affine Model.
32.5 Other Special Constrained Affine Transformations.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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Electronic Access:
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