Automated Analysis and Display of Temporal Sequences of Mammograms

Abstract

Although screening for breast cancer has been effective in detecting cancers, it is not clear that the diagnostic information present in the sequences of screening exams is currently being utilized. In an attempt to unprove diagnostic accuracy, this project is adapting and integrating several novel technologies, under development in our laboratory, into a system for providing mammographers with information about changing tissue patterns, and the corresponding likelihood of malignancy, derived from temporal sequences of images. Our hypotheses are: 1) Sequences of screening mammograms contain information about tissue changes that is not otherwise being exploited in the diagnosis of breast cancer; and, 2) Changing tissue patterns can automatically be identified, and correlated with diagnostic questions. The main objectives of the project are to provide a system, which can be employed at the discretion of mammographers, to: 1) Normalize images to facilitate comparisons; 2) Apply multi-image CAD methods to identify corresponding features between images; 3) Detect and classify trends in temporal sequences; 4) Calculate and present various kinds of parameter images; and, 5) Develop a display system for efficiently presenting sequences of exams. Our expectation is that this display will improve diagnostic performance of mammographers for these cases.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2004
Accession Number
ADA429163

Entities

People

  • Walter F. Good

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Acquisition
  • Algorithms
  • Biomedical Research
  • Breast Cancer
  • Coordinate Systems
  • Databases
  • Detection
  • Diagnostic Imaging
  • Display Systems
  • Health Services
  • Image Processing
  • Image Registration
  • Neoplasms
  • Standards
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Computer Vision.
  • Oncology and Biomarker-Based Cancer Detection.