Many computer aided diagnosis (CAD) systems help radiologist on difficult task of mass detection in a breast mammogram and, besides, they also provide interpretation about detected mass. One of the most crucial information of a mass is its shape and contour, since it provides valuable information about spread ability of a mass. However, accuracy of shape recognition of a mass highly related with the precision of detected mass contours. In this work, we introduce a new segmentation algorithm, breast mass contour segmentation, based on classical seed region growing algorithm to enhance contour of a mass from a given region of interest with ability to adjust threshold value adaptively. The new approach is evaluated over a dataset with 260 masses whose contours are manually annotated by expert radiologists. The performance of the method is evaluated with respect to a set of different evaluation metrics, such as specificity, sensitivity, balanced accuracy, Yassnoff and Hausdorrf error distances. The results obtained from experimentations shows that our method outperforms the other compared methods. All the findings and details of approach are presented in detail.