Segmentation and SVM Classification of Mammograms

The clustered microcalcification on X-ray mammogram provides an important cue for early detection of breast cancer. Texture analysis methods can be applied to detect clustered micro calcifications in digitized mammograms. In this paper a novel two stage method for mammogram segmentation is implemented to facilitate automatic segmentation of micro calcification. The first stage is the Modified combined morphological spectral unsupervised Image segmentation. The first stage includes watershed transform, anisotrophic filtering technique, band pass filtering scheme, gradient synthesisation and complex wavelet transform (CWT) subband extraction. The results of the Modified combined morphological spectral unsupervised Image segmentation scheme is compared with the combined morphological spectral unsupervised Image segmentation scheme. The second stage of the segmentation scheme is the Random walkers segmentation technique. Finally features are derived from the Ridgelet subbands of the segmented image. The cooccurrence matrix features are also used for classification. This paper also implements the support vector machines (SVM) for effective classification of Mammogram into Benign or malignant mammogram. The validation of the classification scheme was performed by using the Receiver operating curve (ROC) analysis, the overall sensitivity of the technique measured by the value of A_z which was found to be 0.928.