Advanced Machine Vision Paradigms for Medical Image Analysis

Advanced Machine Vision Paradigms for Medical Image Analysis

by Tapan K. Gandhi, Siddhartha Bhattacharyya, Sourav De, Debanjan Konar, Sandip Dey
Released August 2020
Publisher(s): Academic Press
ISBN: 9780128192962

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Book description

Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated.

Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs.

  • Explores major emerging trends in technology which are supporting the current advancement of medical image analysis with the help of computational intelligence
  • Highlights the advancement of conventional approaches in the field of medical image processing
  • Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques, as well as their applications in medical image analysis

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Contributors
  7. Preface
  8. Chapter 1. An introductory illustration of medical image analysis
    1. 1. Introduction
    2. 2. Medical image analysis: issues and challenges
    3. 3. Summary of all contributory chapters (challenges and findings)
    4. 4. Discussion and conclusion with future trends
  9. Chapter 2. Computer-aided decision support system for symmetry-based prenatal congenital heart defects
    1. 1. Introduction
    2. 2. Clinical dataset, clinical perspectives of CHD screening and methodology
    3. 3. Results and discussion
    4. 4. Conclusion
  10. Chapter 3. Morphological extreme learning machines applied to the detection and classification of mammary lesions
    1. 1. Introduction
    2. 2. Breast cancer
    3. 3. Extreme learning machines
    4. 4. Proposed methodology
    5. 5. Results
    6. 6. Conclusion
  11. Chapter 4. 4D medical image analysis: a systematic study on applications, challenges, and future research directions
    1. 1. Introduction
    2. 2. Different 4D medical imaging modalities
    3. 3. Exploration of 4D CT
    4. 4. Exploration of 4D ultrasound imaging
    5. 5. Exploration of 4D flow magnetic resonance imaging
    6. 6. Machine learning techniques in 4D medical imaging
    7. 7. Conclusion and future trends
  12. Chapter 5. Comparative analysis of hybrid fusion algorithms using neurocysticercosis, neoplastic, Alzheimer's, and astrocytoma disease affected multimodality medical images
    1. 1. Introduction
    2. 2. Literature survey
    3. 3. Proposed work
    4. 4. Implementations and discussions
    5. 5. Summary
  13. Chapter 6. Binary descriptor design for the automatic detection of coronary arteries using metaheuristics
    1. 1. Introduction
    2. 2. Background
    3. 3. Proposed method
    4. 4. Computational experiments
    5. 5. Concluding remarks
    6. Appendix A
  14. Chapter 7. A cognitive perception on content-based image retrieval using an advanced soft computing paradigm
    1. 1. Introduction
    2. 2. Related works
    3. 3. Methodology
    4. 4. Experimental results
    5. 5. Conclusion
  15. Chapter 8. Early detection of Parkinson's disease using data mining techniques from multimodal clinical data
    1. 1. Introduction
    2. 2. Review of literature
    3. 3. Materials and methods
    4. 4. Voice dataset processing
    5. 5. Spiral dataset preprocessing
    6. 6. Ensemble-based prediction from voice and spiral dataset
    7. 7. Evaluation metrics
    8. 8. Experimental results
    9. 9. Conclusion
  16. Chapter 9. Contrast improvement of medical images using advanced fuzzy logic-based technique
    1. 1. Introduction
    2. 2. Review of the recent works and motivation toward the proposed work
    3. 3. Steps involved in the proposed approach
    4. 4. Methods and materials
    5. 5. Experiment and result discussion
    6. 6. Subjective evaluations
    7. 7. Objective evaluation
    8. 8. Time comparison
    9. 9. Conclusion
  17. Chapter 10. Bone age assessment using metric learning on small dataset of hand radiographs
    1. 1. Introduction
    2. 2. Motivation
    3. 3. Related work
    4. 4. Methodology
    5. 5. Results
    6. 6. Discussion
    7. 7. Conclusion
  18. Chapter 11. Conclusion and future research directions
    1. 1. Concluding remarks
    2. 2. Future avenues
  19. Index

Product information

  • Title: Advanced Machine Vision Paradigms for Medical Image Analysis
  • Author(s): Tapan K. Gandhi, Siddhartha Bhattacharyya, Sourav De, Debanjan Konar, Sandip Dey
  • Release date: August 2020
  • Publisher(s): Academic Press
  • ISBN: 9780128192962