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

Atam Dhawan, Yateen Chitre, Christine Bonasso, Kevin Wheeler

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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 languageEnglish (US)
Pages (from-to)535-536
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Issue number1
StatePublished - Dec 1 1995
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Bioengineering

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