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Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing

Book Description

Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing disseminates knowledge regarding high performance computing for medical applications and bioinformatics. This critical reference source contains a valuable collection of cutting-edge research chapters for those working in the broad field of medical informatics and bioinformatics.

Table of Contents

  1. Cover
  2. Title Page
  3. Chapter 1: Techniques for Medical Image Segmentation
    1. ABSTRACT
    2. INTRODUCTION
    3. PROGRESS IN THE PREVIOUS YEARS
    4. MOST RECENTLY PROPOSED SOLUTIONS
    5. EVALUATION OF SELECTED METHODS
    6. CONCLUSIONS
  4. Chapter 2: Medical Image Segmentation and Tracking Through the Maximisation or the Minimisation of Divergence Between PDFs
    1. Abstract
    2. 1 INTRODUCTION
    3. 2 OPTIMIZATION OF DIVERGENCES BETWEEN PDFS
    4. 3 A GENERAL RESULT FOR PARAMETRIC PDFS WITHIN THE EXPONENTIAL FAMILY
    5. 4 A GENERAL RESULT FOR NON PARAMETRIC PDFS
    6. 5 MAXIMIZATION OF DISTANCES BETWEEN PARAMETRIC PDFS FOR SEGMENTATION
    7. 6 MINIMIZATION OF DISTANCES BETWEEN NON PARAMETRIC PDFS FOR REGION TRACKING
    8. 7 NUMERICAL IMPLEMENTATION
    9. 8 EXPERIMENTAL RESULTS
    10. 9 CONCLUSION
    11. Appendix A: Proof of Theorem 1
    12. Appendix B: Proof of Lemma 1
    13. Appendix C: Proof of Lemma 2
  5. Chapter 3: Modeling and Simulation of Deep Brain Stimulation in Parkinson’s Disease
    1. ABSTRACT
    2. INTRODUCTION: PARKINSON’S DISEASE (PD)
    3. BACKGROUND: COMPUTATIONAL MODELING OF BASAL GANGLIA (BG) FUNCTION
    4. THE CONNECTIONS IN BASAL GANGLIA
    5. PATHOPHYSIOLOGY OF PARKINSON’S DISEASE
    6. DEEP BRAIN STIMULATION (DBS)
    7. COMPUTATIONAL MODELING OF PARKINSON’S DISEASE AND DBS AT A CELLULAR LEVEL
    8. SYSTEM LEVEL MODELS OF BASAL GANGLIA
    9. CONCLUSION
    10. FUTURE RESEARCH DIRECTIONS
  6. Chapter 4: High-Performance Image Reconstruction (HPIR) in Three Dimensions
    1. ABSTRACT
    2. 1 INTRODUCTION
    3. 2 CT Reconstruction in Hospitals
    4. 3 RECONSTRUCTION ALGORITHMS
    5. 4 High Performance Computing Platforms
    6. 5 CONCLUSION
  7. Chapter 5: Compression of Surface Meshes
    1. 1.1 Abstract
    2. 1.2 INTRODUCTION
    3. 1.3 SHORT SURVEY IN 3D SURFACE COMPRESSION
    4. 1.4 BACKGROUND
    5. 1.5 BIT ALLOCATION PROCESS
    6. 1.6 MODEL-BASED ALGORITHM
    7. 1.7 EXPERIMENTATIONS AND DISCUSSIONS
    8. 1.8 CONCLUSIONS
    9. 1.9 FUTURE RESEARCH DIRECTIONS
  8. Chapter 6: The Role of Self-Similarity for Computer Aided Detection Based on Mammogram Analysis
    1. Abstract
    2. INTRODUCTION
    3. BACKGROUND
    4. CLINICAL ASPECTS OF MAMMOGRAPHY
    5. MAMMOGRAM ANALYSIS THROUGH SELF-SIMILARITY
    6. CONCLUSION
  9. Chapter 7: Volumetric Texture Analysis in Biomedical Imaging
    1. Abstract
    2. INTRODUCTION
    3. Texture Analysis: Generating a Measurement Space
    4. CONCLUSION
    5. FUTURE RESEARCH DIRECTIONS
  10. Chapter 8: Analysis of Doppler Embolic Signals
    1. Abstract
    2. Introduction
    3. Background
    4. CURRENT DIAGNOSTIC APPROACHES TO TCD EMBOLIC ANALYSIS
    5. High-Performance Implementations
    6. Concluding Remarks
  11. Chapter 9: Massive Data Classification of Neural Responses
    1. Abstract
    2. 1. Introduction
    3. 2. The Classification Algorithm
    4. 3. Platform Architecture for Large-Scale Classification
    5. 4. Implementation and Evaluation
    6. 6. Conclusion
  12. Compilation of References