Detection of microcalcification clusters using neural networks

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

Detection of microcalcification clusters using neural networks

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

School of Medicine

Research output: Contribution to journal › Conference article › peer-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 language	English (US)
Pages (from-to)	590-591
Number of pages	2
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	16
Issue number	pt 1
State	Published - Dec 1 1994
Event	Proceedings 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 1994 → Nov 6 1994

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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title = "Detection of microcalcification clusters using neural networks",

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.",

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AU - Bankman, Isaac N.

AU - Tsai, John

AU - Kim, Dong W.

AU - Gatewood, Olga B.

AU - Brody, William R.

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N2 - 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.

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JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

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