Mass Classification with Level Set Segmentation and Shape Analysis for Breast Cancer Diagnosis Using Mammography

Masses are the typical signs of breast cancer. Correctly classifying mammographic masses as malignant or benign can assist radiologists to diagnosis breast cancer and can reduce the unnecessary biopsy without increasing false negatives. In this paper, we investigate the classification of masses with level set segmentation and shape analysis. Based on the initial contour guided by the radiologist, level set segmentation is used to deform the contour and achieve the final segmentation. Shape features are extracted from the boundaries of segmented regions. Linear discriminant analysis and support vector machine are investigated for classification. A dataset consists of 292 ROIs from DDSM mammogram images were used for experiments. The method based on Fourier descriptor of normalized accumulative angle achieved a high accuracy of Az=0.8803. The experimental results show that Fourier descriptor of normalized accumulative angle is an effective feature for the classification of masses in mammogram.