GAUSSIAN MIXTURE MODEL BASED CLASSIFICATION OF MICROCALCIFICATION IN MAMMOGRAMS USING DYADIC WAVELET TRANSFORM

Breast cancer is a serious health related issue for women in the world. Cancer detected at premature stages has a higher probability of being cured, whe reas at advanced stages chances of survival are ble ak. Screening programs aid in detecting potential breas t cancer at early stages of the disease. Among the various screening programs, mammography is the proven standard for screening breast cancer, because even small tumors can be detected on mammograms. In this study, a novel feature extraction technique based on dyadic wavelet transform for classificatio n of microcalcification in digital mammograms is proposed. In the feature extraction module, the hig h frequency sub-bands obtained from the decomposition of dyadic wavelet transform is used t o form innovative sub-bands. From the newly constructed sub-bands, the features such as energy and entropy are computed. In the classification module, the extracted features are fed into a Gauss ian Mixture Model (GMM) classifier and the severity of given microcalcification; benign or malignant ar e given. A classification accuracy of 95.5% is obtained using the proposed approach on DDSM database.