Detection of microcalcification clusters using neural networks

Isaac N. Bankman, John Tsai, Dong W. Kim, Olga B. Gatewood, William R. Brody

Research output: Contribution to journalConference articlepeer-review

Abstract

One of the earliest mammographic signs of breast cancer, a cluster of microcalcifications, is difficult to detect visually, due to the small size of microcalcifications and their resemblance to other bright structures in mammograms. A fully automated algorithm that we developed for detecting clusters of microcalcifications extracts features that represent individual microstructures using the contour map of the mammogram. This allows computations without using predetermined areas of interest (kernels). The extracted features quantify visual recognition criteria. Microcalcifications are discriminated from other microstructures using multi-layer feedforward neural networks whose inputs are the extracted features.

Original languageEnglish (US)
Pages (from-to)590-591
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume16
Issue numberpt 1
StatePublished - Dec 1 1994
EventProceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 1 (of 2) - Baltimore, MD, USA
Duration: Nov 3 1994Nov 6 1994

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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