Development of Methods for Computer-Assisted Interpretation of Digital Mammograms for Early Breast Cancer Detection.

Abstract

The goal of tha research was to develop a computer-vision module as an aid to radiologists the specific aims: (1) Further development of advanced computerized schemes for the detection and classification of masses and microcalcifications in digital mammograms, including quantitative analysis of the radiographic characteristics and the decision-making processes used by radiologists in making a decision with respect to the likelihood of malignancy and choosing the appropriate course of action. (2) Development of a dedicated intelligent modular system with man-machine interfaces and fast computation times appropriate for the effective use of the computer-vision schemes. (3) Evaluation of the efficacy and efficiency of the module using a large clinical database. The significance of this research is that if the detectability of cancers can be increased by employing a computer to aid the radiologist's diagnosis, then the treatment of patients with cancer an be initiated earlier and their chance of survival improved. Systematic introduction of computer-vision tools to radiologists that is presented in this proposal requires minimal modification to the current reading habits of radiologists. When digital mammographic imaging units become commonplace, the computer-vision module can be interfaced to electronic, filmless medical imaging reading areas.

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Document Details

Document Type
Technical Report
Publication Date
Mar 27, 1995
Accession Number
ADA295502

Entities

People

  • Maryellen Lissak Giger

Organizations

  • University of Chicago

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Breast Cancer
  • Cancer
  • Computer Vision
  • Computers
  • Databases
  • Detection
  • Digital Images
  • Digital Information
  • Feature Extraction
  • Gray Scale
  • Health Services
  • Image Processing
  • Information Science
  • Medical Personnel
  • Pattern Recognition
  • Two Dimensional

Fields of Study

  • Medicine

Readers

  • Image Processing and Computer Vision.
  • Oncology and Biomarker-Based Cancer Detection.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • Microelectronics