rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Soft computing based medical image analysis
  name:
    name part: Dey, Nilanjan
    name part: 1984-
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Ashour, Amira
    name part: 1975-
    role:
      role term: author.
  name:
    name part: Shi, Fuquian
    role:
      role term: author.
  name:
    name part: Balas, Valentina Emilia
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2018
    edition: First edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: 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.
  table of contents: 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
  table of contents: 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
  table of contents: 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
  table of contents: 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
  table of contents: 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
  note: Nilanjan Dey, Amira S. Ashour, Fuquian Shi, Valentina E. Balas.
  note: Includes bibliographical references and index.
  subject:
    topic: Diagnostic imaging
  subject:
    topic: Diagnostic imaging
    topic: Digital techniques
  subject:
    topic: Diagnostic Imaging
  subject:
    topic: Imagerie pour le diagnostic
  subject:
    topic: Imagerie pour le diagnostic
    topic: Techniques numériques
  subject:
    topic: HEALTH & FITNESS
    topic: Diseases
    topic: General
  subject:
    topic: MEDICAL
    topic: Clinical Medicine
  subject:
    topic: MEDICAL
    topic: Diseases
  subject:
    topic: MEDICAL
    topic: Evidence-Based Medicine
  subject:
    topic: MEDICAL
    topic: Internal Medicine
  subject:
    topic: Diagnostic imaging
  subject:
    topic: Diagnostic imaging
    topic: Digital techniques
  classification: RC78.7.D53
  classification: 616.07/54
  related item:
    identifier: (OCoLC)1001456605
  identifier: 9780128131749
  identifier: 0128131748
  identifier: 9780128130872
  identifier: 0128130873
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: N$T
    record creation date: 180123
    record change date: 20260327153019.4
    record identifier: in00005762894
    language of cataloging:
      language term: eng