Improved Mammographic Technique for Breast Cancer Diagnosis.

Abstract

The goal of the proposed research is to improve the sensitivity of cancer detection in mixed or dense breasts through optimization of mammographic techniques. We propose to develop a novel exposure equalization system that preferentially reduces the incident x-ray intensity in the peripheral region of the breast, thereby alleviating the problem of limited latitude of x-ray detectors. Optimal imaging technique can then be developed for improving image quality throughout the entire breast. We have performed the following studies in the second year of the funding period: (1) collected a large data base for analysis of breast shapes and exposure profiles, (2) evaluated the accuracy of the automated breast border detection program, (3) evaluated the adequacy of two polynomial models for fitting the breast shapes and classification, (4) developed a clustering procedure for classifying the breast shapes into a small number of classes, (5) developed an automated breast shape classification program to prepare for on-line analysis of patient breast shape, (6) developed a Monte Carlo simulation model for mammographic imaging (7) evaluated experimentally and by Monte Carlo simulation the scatter fraction on the imaging plane and studied the primary exposure profiles in a given breast shape class. The results of these studies will be useful for the implementation of the filter device as planned in the future years.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1996
Accession Number
ADA317908

Entities

People

  • Heang-Ping Chan

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Accuracy
  • Breast Cancer
  • Classification
  • Clustering
  • Computer Programs
  • Data Science
  • Databases
  • Detection
  • Detectors
  • Equalization
  • Grids
  • Imaging Techniques
  • Information Science
  • Monte Carlo Method
  • Simulations
  • Statistical Analysis
  • X Rays

Fields of Study

  • Physics

Readers

  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.
  • Oncology and Biomarker-Based Cancer Detection.