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Machine Learning in Computer-Aided Diagnosis

Book Description

Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis provides a comprehensive overview of machine learning research and technology in medical decision-making based on medical images. This book covers major technical advancements and research findings in the field of Computer-Aided Diagnosis (CAD). As it demonstrates the practical applications of CAD, this book is a useful reference for professors in engineering and medical schools, students in engineering and applied-science, medical students, medical engineers, researchers in industry, academia, and health science, radiologists, cardiologists, surgeons, and healthcare professionals.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  5. Dedication
  6. Foreword
  7. Preface
  8. Acknowledgment
  9. Section 1: Breast Imaging
    1. Chapter 1: Robustness Studies of Ultrasound CADx in Breast Cancer Diagnosis
      1. 1. ABSTRACT
      2. 2. CADX FOR BREAST ULTRASOUND
      3. 3. GENERAL RATIONALE FOR ROBUSTNESS STUDIES
      4. 4. ROBUSTNESS OVER ACQUISITION SYSTEM VARIATION
      5. 5. ROBUSTNESS OVER PATIENT VARIATION
      6. 6. ROBUSTNESS OVER USER VARIATION
      7. 7. COMPUTER SELF-ASSESSMENT OF ROBUSTNESS
      8. 8. CONCLUSION
    2. Chapter 2: Digital Image Processing and Machine Learning Techniques for the Detection of Architectural Distortion in Prior Mammograms1
      1. Abstract
      2. INTRODUCTION
      3. DETECTION OF ARCHITECTURAL DISTORTION
      4. EXPERIMENTAL SETUP AND DATASET
      5. METHODS
      6. RESULTS
      7. DISCUSSION
      8. CONCLUSION
    3. Chapter 3: Computer-Aided Detection and Diagnosis for 3D X-Ray Based Breast Imaging
      1. Abstract
      2. INTRODUCTION
      3. RECENT ADVANCES IN 3D X-RAY BASED BREAST IMAGING
      4. COMPUTER-AIDED DETECTION AND DIAGNOSIS FOR 3D X-RAY BASED BREAST IMAGING
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    4. Chapter 4: Relevance Feedback as New Tool for Computer-Aided Diagnosis in Image Databases
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. MACHINE LEARNING FOR CBIR
      5. CBIR BY RELAVENCE FEEDBACK
      6. PERFORMANCE EVALUATION STUDY
      7. EXPERIMENTAL RESULTS
      8. CONCLUSION
    5. Chapter 5: Advanced Fuzzy Methods for Mammography Image Analysis
      1. Abstract
      2. INTRODUCTION
      3. BREAST CANCER DIAGNOSIS USING MAMMOGRAPHY AND COMPUTER-AIDED DIAGNOSIS SYSTEMS
      4. Fuzzy Concept for Mammography Images
      5. DISCUSSION
      6. CONCLUSION
  10. Section 2: Thoracic Imaging
    1. Chapter 6: Computerized Detection of Lung Nodules on Chest Radiographs
      1. Abstract
      2. INTRODUCTION
      3. RELATED WORK
      4. COMPUTERIZED DETECTION OF LUNG NODULES IN CXR1
      5. CREATION OF VDE IMAGES
      6. TRAINING AND TESTING METHOD FOR THE ANATOMICALLY SPECIFIC MULTIPLE MTANNS
      7. EVALUATION OF OUR CADE SCHEME BY USE OF VDE TECHNOLOGY
      8. DISCUSSION
      9. CONCLUSION
    2. Chapter 7: Techniques for the Automated Segmentation of Lung in Thoracic Computed Tomography Scans
      1. Abstract
      2. INTRODUCTION
      3. OVERVIEW OF THE LUNG SEGMENTATION METHOD
      4. MODULE REMOVING FALSE CONNECTIONS BETWEEN LUNGS
      5. MODULE HANDLING INCORRECT CONCAVITIES CAUSED BY VESSELS AND AIRWAY
      6. MODULE HANDLING BOWEL
      7. CONCLUSION
    3. Chapter 8: Clinical Machine Learning in Action
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. CIRCUS: CLINICAL INFRASTRUCTURE FOR RADIOLOGIC COMPUTATION OF UNITED SOLUTIONS
      5. LONG-TERM EXPERIENCE WITH CADE BASED ON THE CIRCUS SYSTEM
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
  11. Section 3: Abdominal Imaging
    1. Chapter 9: Computer-Aided Detection of Polyps in CT Colonography by Means of Feature Selection and Massive-Training Support Vector Regression
      1. Abstract
      2. INTRODUCTION
      3. MATERIALS AND METHODS
      4. RESULTS
      5. CONCLUSION
    2. Chapter 10: Content-Based Image Retrieval for Medical Image Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. CBIR TECHNIQUES
      4. CBIR IN MEDICAL IMAGING
      5. DISCUSSION AND FUTURE DIRECTIONS
    3. Chapter 11: A Model-Driven Bayesian Method for Polyp Detection and False Positive Suppression in CT Colonography Computer-Aided Detection
      1. Abstract
      2. 1. INTRODUCTION
      3. 2. METHOD
      4. 3. EXPERIMENTAL RESULTS AND DISCUSSION
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    4. Chapter 12: Computer-Aided Image Analysis and Detection of Prostate Cancer
      1. Abstract
      2. BACKGROUND
      3. MATERIALS AND METHODS
      4. RESULTS
      5. DISCUSSION
      6. FUTURE RESEARCH
      7. CONCLUSION
  12. Section 4: Brain Imaging
    1. Chapter 13: Magnetic Resonance Image Analysis for Brain CAD Systems with Machine Learning
      1. Abstract
      2. BACKGROUND
      3. IMAGE FEATURE EXTRACTION
      4. CLASSIFICATION PROCESS USING MACHINE LEARNING
      5. EVALUATION OF CAD SYSTEMS
      6. DEVELOPMENT CYCLE OF CAD SYSTEMS
    2. Chapter 14: CADrx for GBM Brain Tumors
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    3. Chapter 15: Autism Diagnostics by 3D Shape Analysis of the Corpus Callosum
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODS
      5. RESULTS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
    4. Chapter 16: Neuroimage Classification for Early Diagnosis of Alzheimer’s Disease
      1. Abstract
      2. INTRODUCTION
      3. METHODOLOGY
      4. APPLICATIONS
      5. CONCLUSION
    5. Chapter 17: Manifold Learning for Medical Image Registration, Segmentation, and Classification
      1. Abstract
      2. INTRODUCTION
      3. MANIFOLD LEARNING TECHNIQUES
      4. APPLYING MANIFOLD LEARNING METHODS TO MEDICAL IMAGES
      5. APPLICATIONS OF MANIFOLD LEARNING IN MEDICAL IMAGING
      6. FUTURE DIRECTIONS
      7. CONCLUSION
  13. Section 5: Body Imaging
    1. Chapter 18: Learning Manifolds
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND: MANIFOLD LEARNING
      4. DESIGNING MANIFOLD LEARNING METHODS FOR MEDICAL APPLICATIONS
      5. DISCUSSION
      6. CONCLUSION
    2. Chapter 19: Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. ENSEMBLE-LEARNING-BASED AUTOMATED ORGAN LOCALIZATION IN CT IMAGES
      5. MAJORITY VOTING METHOD FOR THE ESTIMATION OF FINAL TARGET LOCATION
      6. PERFORMACE EVALUATION
      7. RESULTS
      8. DISCUSSIONS
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
    3. Chapter 20: Applications of Machine Learning in Real-Time Tumor Localization
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. MACHINE LEARNING TECHNIQUES FOR TUMOR LOCALIZATION
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
  14. Compilation of References
  15. About the Contributors
  16. Index