Abstract
Breast cancer is the leading cause of death among women. Mammography is the only effective and viable technique to detect breast cancer, sometimes before the cancer becomes invasive. About 30% to 50% of breast cancers demonstrate clustered microcalcifications. We investigate the potential of using second-order histogram textural features for their correlation with malignancy. A combination of image structure features extracted from the second histogram was used with binary cluster features extracted from segmented calcifications. Several architectures of neural networks were used for analyzing the features. The neural network 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) | 592-593 |
| 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 - 1994 |
| Externally published | Yes |
| 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 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics