Abstract
Mammography has been established as the only effective and viable technique to detect breast cancer especially in the case of nonpalpable and minimal tumors. About 30% to 50% of breast cancers demonstrate deposits of calcium called microcalcifications. We investigate the potential of using textural features for their correlation with malignancy. A combination of global texture features extracted from the second histogram was combined with local texture features obtained from a wavelet decomposition of the regions containing the calcifications. The performance of the radial-basis-function neural network was compared to the standard multilayered perceptron. The neural networks yielded good results for the classification of hard-to-diagnose cases of mammographic microcalcification into benign and malignant categories using the selected set of features.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 535-536 |
| Number of pages | 2 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 17 |
| Issue number | 1 |
| State | Published - 1995 |
| Externally published | Yes |
| Event | Proceedings of the 1995 IEEE Engineering in Medicine and Biology 17th Annual Conference and 21st Canadian Medical and Biological Engineering Conference. Part 2 (of 2) - Montreal, Can Duration: Sep 20 1995 → Sep 23 1995 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics