Breast cancer is one of the most serious diseases greatly threatening human′s health. A patient will not miss the best time for treatment only with early detection and early diagnosis. Mass is the most important and common lesion of breast, so breast mass feature extraction is helpful to improve the efficiency and accuracy of diagnosis. The past algorithms do not consider spatial information of〖JP+2〗 mass images, resulting in low classification accuracy. Aiming at this problem, a new breast mass feature extraction algorithm is proposed based on the edge of neighborhood. It combines the Chan Vese active contour model with the bag of words. The adaptive parameters regulation methods are designed to control edges of mass images. The final representation can be obtained by combining or weighting those features in the neighborhood. Experimental results show that the proposed methods can achieve a better classification accuracy.