We compared different edge detection algorithms and found that the 'Marr-Hildreth' edge detection have the best performance for macrocalcification shape preservation in our MCCs detection system. Edge detection is one of the most commonly used operations in image analysis. The edges form the outline of the macrocalcifications. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the macrocalcifications can be located and the areas, perimeters, and shapes can be measured. So edge detection is one of the effect methods to preserve the shape of microcalcifications. Based on the edge enhancement method, a new mixed feature multistage method has been developed for improving the false positive (FP) reduction performance. Eleven features were extracted from both spatial and morphology domains in order to describe the micro-calcification clusters (MCCs) from different perspectives. These features are grouped into three categories: gray-level description, shape description and clusters description. This method was combined with neural network used in our false positive reduction, that reduce the false positive from 3.1/image to 0.1/image in 50 full field digital mammograms, The 50 mammograms are with 24 normal images and 26 abnormal images, including 41 microcalcification clusters in our database.