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Artificial neural network based classification of mammographic microcalcifications using image structure and cluster features
- Source :
- Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
- Publication Year :
- 2002
- Publisher :
- IEEE, 2002.
-
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 malignant categories using the selected set of features. >
- Subjects :
- Mammographic microcalcification
Artificial neural network
medicine.diagnostic_test
Contextual image classification
Computer science
business.industry
Feature extraction
Cancer
Pattern recognition
Image segmentation
Malignancy
medicine.disease
Breast cancer
Image texture
Histogram
medicine
Mammography
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Accession number :
- edsair.doi...........a4628244b4122f7a6f1333fa4f3d14b6