Contents

• List of Figures
• List of Tables
• Introduction
  • Breast Cancer
  • Mammography
    • Mammographic Abnormalities
  • Digital Mammography
    • Image Acquisition
    • Image Storage
    • Image Display
  • Computer-Aided Systems
    • The Benefits of CAD
    • Commercial CADs
  • Scope of the Research
    • HRIMAC Project
  • Objectives of the Thesis
  • Thesis Outline
    • A New Framework for Mass Detection
    • Document Overview
  
  
• A Review of Automatic Mass Segmentation Techniques
  • Introduction
  • Breast Profile Segmentation
  • Mass Segmentation Using One Single View
    • Region-Based Methods
    • Contour-Based Methods
    • Clustering and Thresholding Methods
    • Model-Based Methods
  • Mass Segmentation Using Two or More Images
    • Comparison of Left and Right Mammograms
    • Comparison of Two Mammographic Views
    • Temporal Comparison of Mammograms
  • Evaluation of Mass Segmentation Methods
    • Evaluated Mass Segmentation Methods
    • Evaluation Methodology: ROC and FROC Curves
    • Mass Segmentation Results
  • Discussion
  
  
• Breast Density Classification
  • Introduction
  • A Survey on Automatic Breast Density Classification
  • A New Proposal for Automatic Breast Density Classification
    • Finding Regions with Similar Tissue
    • Extracted Features
    • Classification
  • Results
    • MIAS Database
    • DDSM Database
    • The Importance of the Segmentation Step
  • Discussion
    • Comparison with the Works which Classifies into BIRADS Categories
    • Conclusions
  
  
• Mass Segmentation Using Shape and Size Lesion Information
  • Introduction
  • A Brief Review on Deformable Template Models
  • From Eigenfaces to Eigenmasses
    • Eigenfaces
    • Eigenmasses and Eigenrois
  • Probabilistic Mass Contour Template
  • Template Based Detection
    • Prior Distribution
    • Likelihood
    • Posterior Probability Density
    • Final Considerations
  • Results
    • MIAS Database
    • Málaga Database
  • Discussion
  
  
• False Positive Reduction
  • Introduction
  • PCA-Based False Positive Reduction
  • 2DPCA-Based False Positive Reduction
  • Evaluation of the False Positive Approaches
    • MIAS Database
    • DDSM Database
  • Combining the Bayesian Pattern Matching and the False Positive Reduction Step
    • MIAS Database
    • Training and Testing using Different Databases
    • Computational Cost
  • Discussion
  
  
• Automatic Mass Segmentation using Breast Density Information
  • Introduction
  • Including Breast Density Information in our Mass Detection Approach
  • Results Obtained Including Breast Tissue Information
  • False Positive Reduction Step with Breast Density Information
    • Comparison of the Method with Existing Approaches
  • Testing the Approach Using a Full-Field Digital Mammographic Database
  • Discussion
  
  
• Conclusions
  • Summary of the Thesis
    • Contributions
  • Further Work
    • Increasing the Reliability of the Proposal
    • Future Research Lines Departing from this Thesis
    • Technological Further Work
  • Related Publications
  
  
• Breast Profile Segmentation
  • Introduction
  • A Fast Breast Segmentation Algorithm with Pectoral Muscle Suppression
  • A Contour-Based Approach to Breast Skin-Line Segementation
    • Skin-Line Detection in Scale Space
    • Seed Point
    • Contour Growing
  
  
• A Brief Description of the Used Mammographic Databases
  • Introduction
  • MIAS
    • Database Characteristics
    • Used Mammograms Containing Masses
  • DDSM
    • Database Characteristics
    • Used Mammograms Containing Masses
  • Málaga Database
    • Database Characteristics
    • Used Mammograms Containing Masses
  • Trueta Database
    • Image Characteristics
    • Used Mammograms Containing Masses
  
  
• Evaluation Methodologies
  • Introduction
  • Evaluation of Classifiers
    • Confusion Matrices
    • ROC Analysis
  • Detection Evaluation
    • ROC Analysis
    • FROC Analysis
  
  
• Bibliography