a novel computer aided breast mass detection scheme based on morphological enhancement and slic superpixel segmentation purpose to develop a computeraided detection cad scheme for mass detection on digitized mammograms that achieves a high sensitivity while maintaining a low false positive fp rate using morphological enhancement and simple linear iterative clustering slic methodmethods the authors developed a multiple stage method for breast mass detection the proposed cad scheme consists of five major components 1 preprocessing based on morphological enhancement which enhances masslike patterns while removing unrelated background clutters 2 segmentation of mass candidates based on the slic method which groups mass and background tissue into different regions 3 prescreening of suspicious regions using rulebased classification that eliminates regions unlikely to represent masses 4 potential lesion contour refinement based on distance regularized level set evolution and 5 fp reduction based on feature extraction and an ensemble of undersampled support vector machines two datasets were built to design and evaluate the system a mass dataset containing 187 cases 386 mammograms and a nonmass dataset containing 88 mammograms all cases were acquired from the digital database for screening mammography ddsm approximately two thirds of the available masses were used for training the system and the remaining masses and nonmass dataset were used for testingresults testing of the proposed cad system on the mass dataset yielded a massbased sensitivity of 9855 9710 9275 at 084 063 055 fp markimage respectively tested on the nonmass dataset the scheme showed a fp rate of 055 034 030 markimageconclusions the results indicate that the system is promising in improving the performance of current cad systems by reducing fp rate while achieving relatively high sensitivity