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) |
|---|---|
| Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Publisher | IEEE |
| Pages | 590-591 |
| Number of pages | 2 |
| Volume | 16 |
| Edition | pt 1 |
| State | Published - 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 |
Other
| Other | Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 1 (of 2) |
|---|---|
| City | Baltimore, MD, USA |
| Period | 11/3/94 → 11/6/94 |
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ASJC Scopus subject areas
- Bioengineering
Cite this
Detection of microcalcification clusters using neural networks. / Bankman, Isaac N.; Tsai, John; Kim, Dong W.; Gatewood, Olga B.; Brody, William R.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Vol. 16 pt 1. ed. IEEE, 1994. p. 590-591.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detection of microcalcification clusters using neural networks
AU - Bankman, Isaac N.
AU - Tsai, John
AU - Kim, Dong W.
AU - Gatewood, Olga B.
AU - Brody, William R.
PY - 1994
Y1 - 1994
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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0028737182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0028737182&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0028737182
VL - 16
SP - 590
EP - 591
BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PB - IEEE
ER -