http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Soft computing based medical image analysis
Soft Computing Based Medical Image Analysis presents the foremost techniques of soft computing in medical image analysis and processing. It includes image enhancement, segmentation, classification-based soft computing, and their application in diagnostic imaging, as well as an extensive background for the development of intelligent systems based on soft computing used in medical image analysis and processing. The book introduces the theory and concepts of digital image analysis and processing based on soft computing with real-world medical imaging applications. Comparative studies for soft computing based medical imaging techniques and traditional approaches in medicine are addressed, providing flexible and sophisticated application-oriented solutions.
Front Cover; Soft Computing Based Medical Image Analysis; Copyright; Contents; Contributors; Preface; Acknowledgments; Section A: Medical Image Analysis and Processing; Chapter 1: Computing in Medical Image Analysis; 1. Introduction; 2. Medical Image Segmentation Techniques; 2.1. Histogram-Based Segmentation; 2.2. Region-Based Segmentation; 2.3. Split-and-Merge Segmentation; 2.4. Edge-Based Segmentation; 3. Metaheuristics; 3.1. Genetic Algorithm; 3.2. Particle Swarm Optimization; 3.3. Ant Colony Optimization; 3.4. Bat Optimization Algorithm; 4. Segmentation Algorithms for Medical Images
4.1. Metaheuristics-Based Segmentation of Magnetic Resonance Images4.2. Metaheuristics Based Segmentation of Computed Tomography Images; 5. Conclusion; References; Chapter 2: Automated Pathology Image Analysis; 1. Introduction; 2. Need for Quantitative Image Analysis in Pathology; 2.1. Differences in Radiology and Histopathology Automated Techniques; 2.2. Anomalies in Microscopic Images; 3. Histology-Imaging Technologies; 3.1. Light Microscopy; 3.2. Fluorescence Microscopy; 3.3. Confocal Microscopy; 3.4. Hyperspectral and Multispectral Microscopy; 3.5. Electron Microscopy
3.6. Transmission Electron Microscopes3.7. Scanning Electron Microscopes; 4. Automated Pathology Image Analysis; 4.1. Image Preprocessing; 4.1.1. Color Illumination and Normalization; 4.1.2. Image Enhancement; 4.2. Image Segmentation; 4.2.1. Pathology Image Segmentation; 4.3. Feature Extraction; 4.3.1. Pixel-Level Features; 4.3.2. Object-Level Features; 4.3.3. Semantic-Level Features; 4.4. Feature Selection; 4.4.1. Dimensionality Reduction; 4.5. Image Classification; 5. Pathology Image Data Sources; 5.1. The Cancer Genome Atlas; 5.2. Lung Image Database Consortium
5.3. National Biomedical Imaging Archive (NBIA)5.4. Microscopic Blood Cell Image Dataset (ALL-IDB1, ALL-IDB2); 5.5. DTI Database; 6. Discussion; 7. Future Trends and Open Issues; References; Chapter 3: Multiple Kernel-Learning Approach for Medical Image Analysis; 1. Introduction; 2. Related Literature; 3. Nature and Characteristics of Biomedical Images; 3.1. Image Characteristics; 3.2. Medical Imaging Modalities; 3.3. Image Noise; 4. Feature Extraction and Image Descriptors; 4.1. Image Descriptors; 5. Computer-Aided Diagnosis; 6. Kernel-Based Machine Learning; 6.1. Basics in Machine Learning
6.1.1. Classification and Regression6.1.2. Cost Function; 6.2. Similarity Measures and Features; 6.3. Kernels; 6.4. Kernel Trick; 6.5. Kernel Matrix; 7. Multiple Kernel-Learning Model; 7.1. 1-Norm Soft-Margin SVM; 7.2. MKL Optimization Using SDP; 8. Multiple Kernel Learning for Biomedical Image Analysis; 8.1. Open-Source Computer Vision; 8.2. Shogun Machine-Learning Toolbox; 8.3. Case Study; 8.3.1. Dataset; 8.3.2. Experiment; 9. Discussion; 9.1. Limitations; 9.2. Future Challenges; 10. Conclusion; Acknowledgments; References; Further Reading; Section B: Medical Image Enhancement
Nilanjan Dey, Amira S. Ashour, Fuquian Shi, Valentina E. Balas.
Includes bibliographical references and index.