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Conference proceeding
Breast cancer detection using image processing techniques
Proceedings of FUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems, pp.973-976
IEEE International Conference on Fuzzy Systems (Hilton Palacio del Rio, San Antonio, Texas, 05/07/2000–05/10/2000)
2000
Abstract
We describe the use of segmentation with fuzzy models and classification by the crisp "k-nearest" neighbor (knn) algorithm for assisting breast cancer detection in digital mammograms. Our research utilizes images from the "Digital Database for Screening Mammography" (DDSM). We show that supervised and unsupervised methods of segmentation, such as k-nn and "fuzzy c-means" (FCM), in digital mammograms will have high misclassification rates when only intensity is used as the discriminating feature. Adding window means and standard deviations to the feature suite (visually) improves segmentations produced by the k-nn rule. While our results are encouraging, other methods are needed to detect smaller pathologies such as microcalcifications.
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Details
- Title
- Breast cancer detection using image processing techniques
- Publication Details
- Proceedings of FUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems, pp.973-976
- Resource Type
- Conference proceeding
- Conference
- IEEE International Conference on Fuzzy Systems (Hilton Palacio del Rio, San Antonio, Texas, 05/07/2000–05/10/2000)
- Publisher
- IEEE; Piscataway, NJ
- Copyright
- 2000 IEEE
- Identifiers
- 99380090340506600
- Academic Unit
- Public Health; Usha Kundu, MD College of Health
- Language
- English