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Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes

Book Description

Every second, users produce large amounts of image data from medical and satellite imaging systems. Image mining techniques that are capable of extracting useful information from image data are becoming increasingly useful, especially in medicine and the health sciences. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. Highlighting current research intended to advance the medical field, this publication is essential for use by researchers, advanced-level students, academicians, medical professionals, and technology developers. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  6. Preface
    1. RESEARCH TOPICS IN THE FIELD OF BIOMEDICAL IMAGE MINING
    2. OBJECTIVE OF THE BOOK
    3. TARGET AUDIENCE
    4. ORGANIZATION OF THE BOOK
    5. CONCLUSION
    6. REFERENCES
  7. Acknowledgment
  8. Section 1: Introduction to Biomedical Image Mining
    1. Chapter 1: Biomedical Image Processing Overview
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. MEDICAL IMAGING MODALITIES
      4. 3. MINING MEDICAL IMAGE
      5. 4. IMAGE MINING TECHNIQUES
      6. 5. BIOMEDICAL IMAGE PROCESSING
      7. 6. IMAGE INTERPRETATION
      8. 7. CONCLUSION
      9. REFERENCES
  9. Section 2: Biomedical Image Processing
    1. Chapter 2: Application of Genetic Algorithm in Denoising MRI Images Clouded with Rician Noise
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. RICIAN NOISE
      5. GENETIC ALGORITHM
      6. OPERATION OF GENETIC ALGORITHM
      7. METHODOLOGY
      8. RESULTS
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    2. Chapter 3: A Review on Brain Imaging Techniques for BCI Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. DIFFERENT IMAGING TECHNIQUES FOR BCI APPLICATIONS
      4. FUTURE RESEARCH DIRECTIONS
      5. CONCLUSION
      6. REFERENCES
    3. Chapter 4: iCellFusion
      1. ABSTRACT
      2. INTRODUCTION
      3. METHODS
      4. RESULTS AND DISCUSSION
      5. CONCLUSION
      6. ACKNOWLEDGMENT
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    4. Chapter 5: A Simple Non-Invasive Automated Heart Rate Monitoring System Using Facial Images
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. PROPOSED ALGORITHM
      5. RESULTS AND DISSCUSSION OF RESULTS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    5. Chapter 6: Compressed Sensing and Its Application in CT and EEG
      1. ABSTRACT
      2. INTRODUCTION TO COMPRESSED SENSING
      3. SIGNAL MODELS
      4. CONDITIONS FOR SPARSE SIGNAL RECOVERY
      5. RECONSTRUCTION ALGORITHMS
      6. INTRODUCTION TO COMPUTED TOMOGRAPHY
      7. CT IMAGE RECONSTRUCTION
      8. COMPRESSED SENSING IN CT
      9. INTRODUCTION TO ELECTROENCEPHALOGRAPHY
      10. CONCLUSION AND DISCUSSIONS
      11. REFERENCES
      12. ENDNOTE
    6. Chapter 7: Visual Perception from Object Scanning as Revealed by Electrooculography
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FUTURE DIRECTIONS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    7. Chapter 8: Mining of Medical Trends Using Social Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK DONE IN SOCIAL NETWORK ANALYSIS (E-HEALTH)
      4. IMAGE MINING ASPECT IN SOCIAL NETWORK ANALYSIS
      5. PROPOSED FRAME WORK FOR MINING MEDICAL TRENDS USING SOCIAL NETWORK
      6. COMPONENTS OF THE PROPOSED FRAMEWORK
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
  10. Section 3: Information Retrieval in Biomedical Images
    1. Chapter 9: Medical Image Mining Using Fuzzy Connectedness Image Segmentation
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    2. Chapter 10: Classification Approach for Breast Cancer Detection Using Back Propagation Neural Network
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODOLOGY
      5. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    3. Chapter 11: Multimodal Indexing and Information Retrieval in Medical Image Mammographies
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ENDNOTES
    4. Chapter 12: Data Mining-Based CBIR System
      1. ABSTRACT
      2. INTRODUCTION
      3. NEED OF MULTIMEDIA MINING
      4. INTRODUCTION TO MULTIMEDIA DATA MINING
      5. POTENTIAL APPLICATIONS OF MULTIMEDIA MINING
      6. IMPORTANCE OF CONTENT BASED IMAGE RETRIEVAL (CBIR)
      7. TYPES OF CBIR BASED IMAGE RETRIEVAL
      8. CBIR METHODOLOGIES
      9. METHODS FOR FEATURE EXTRACTION
      10. APPLICATION OF CBIR IN MEDICAL
      11. CURRENT ISSUES WITH CBIR SYSTEMS
      12. DATA MINING IN CBIR
      13. DATA MINING
      14. DATA MINING TECHNIQUES
      15. USAGE OF DATA MINING TECHNIQUES TO DEVELOP CBIR
      16. TECHNIQUES USED IN MEDICAL IMAGE RETRIEVAL
      17. CONCLUSION
      18. REFERENCES
    5. Chapter 13: Computational Intelligence-Based Cell Nuclei Segmentation from Pap Smear Images
      1. ABSTRACT
      2. INTRODUCTION
      3. REVIEW ON EXISTING SEGMENTATION METHODS FOR NUCLEI SEGMENTATION
      4. THRESHOLD BASED SEGMENTATION
      5. PAP SMEAR IMAGE SEGMENTATION
      6. EXPERIMENTAL RESULTS AND DISCUSSION
      7. REFERENCES
  11. Section 4: Prediction and Simulation in Biomedical Image Mining
    1. Chapter 14: Molecular Dynamics Simulations for Biological Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MOLECULAR MODELING
      5. PHYSICS BEHIND MOLECULAR DYNAMICS SIMULATION
      6. STRUCTURAL VISUALIZATION
      7. TIME SERIES CALCULATION
      8. COMPUTATIONAL CHALLENGES
      9. APPLICATIONS OF MOLECULAR DYNAMICS SIMULATIONS
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
    2. Chapter 15: Prediction and Detection of Epileptic Seizure
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE ARTICLE
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    3. Chapter 16: Electromyography-Based Functional Electrical Stimulation (FES) in Rehabilitation
      1. ABSTRACT
      2. INTRODUCTION
      3. FUNCTIONAL ELECTRICAL STIMULATION (FES)
      4. NEED OF FES
      5. PURPOSE AND USEFULNESS OF EMG BASED FES
      6. EMG BASED FES SYSTEMS
      7. OTHER METHODS OF FES
      8. FES DEVICES
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. REFERENCES
  12. Compilation of References
  13. About the Contributors