A New CAD System for Breast Microcalcifications Diagnosis

Breast cancer is one of the most deadly cancers in the world, especially among women. With no identified causes and absence of effective treatment, early detection remains necessary to limit the damages and provide possible cure. Submitting women with family antecedent to mammography periodically can provide an early diagnosis of breast tumors. Computer Aided Diagnosis (CAD) is a powerful tool that can help radiologists improving their diagnostic accuracy at earlier stages. Several works have been developed in order to analyze digital mammographies, detect possible lesions (especially masses and microcalcifications) and evaluate their malignancy. In this paper a new approach of breast microcalcifications diagnosis on digital mammograms is introduced. The proposed approach begins with a preprocessing procedure aiming artifacts and pectoral muscle removal based on morphologic operators and contrast enhancement based on galactophorous tree interpolation. The second step of the proposed CAD system consists on segmenting microcalcifications clusters, using Generalized Gaussian Density (GGD) estimation and a Bayesian backpropagation neural network. The last step is microcalcifications characterization using morphologic features which are used to feed a neuro-fuzzy system to classify the detected breast microcalcifications into benign and malignant classes. Keywords—Artifacts and pectoral muscle removal; Bayesian back-propagation neural network; Breast microcalcifications; CAD system; Digital mammograms; Galactophorous tree interpolation; GGD estimation; Morphologic features; Neuro-fuzzy system