A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation




To develop a computer-aided 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) method.


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 mass-like 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 rule-based 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 testing.


Testing of the proposed CAD system on the mass dataset yielded a mass-based sensitivity of 98.55%, 97.10%, 92.75% at 0.84, 0.63, 0.55 FP mark/image, respectively. Tested on the nonmass dataset, the scheme showed a FP rate of 0.55, 0.34, 0.30 mark/image.


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.