Cargando…
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 f...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press,
[2018]
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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