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Radial-basis-function based classification of mammographic microcalcifications using texture features

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4 Author(s)
Dhawan, Atam P. ; Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA ; Chitre, Y. ; Bonasso, C. ; Wheeler, K.

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

Published in:

Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference  (Volume:1 )

Date of Conference:

20-25 Sep 1995