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The purpose of this research was evaluating novel shape and texture feature’ efficiency in classification of benign and malignant breast masses in sonography images. First, mass regions were extracted from the region of interest (ROI) sub-image by implementing a new hybrid segmentation approach based on level set algorithms. Then two left and right side areas of the masses are elicited. After that, six features (Eccentricity_feature, Solidity_feature, DeferenceArea_Hull_Rectangular, DeferenceArea_Mass_Rectangular, Cross-correlation-left and Cross-correlation-right) based on shape, texture and region characteristics of the masses were extracted for further classification. Finally a support vector machine (SVM) classifier was utilized to classify breast masses. The leave-one-case-out protocol was utilized on a database of eighty pathologically-proven breast sonographic images of patients (forty-seven …