Automatic mass detection in mammography images using particle swarm optimization and functional diversity indexes

This paper proposes a computational method to assist in detection of masses in dense and non-dense breasts on mammography images. The proposed methodology is divided into six steps. In summary, the first step consist of the images acquisition that was obtained from the Digital Database for Screening Mammography (DDSM). In the second step, a preprocessing is performed in order to remove noises and enhance the images. In the third step, the segmentation is performed to find the regions of interest (ROIs) that are candidates for masses using Particle Swarm Optimization (PSO). The fourth step consists in the first false positives reduction based on reduction by distance and Graph Clustering. The fifth step is the second false positive reduction based on texture features using functional diversity indexes. Finally, in the sixth step, the support vector machine (SVM) is used to classify ROIs in whether mass or non-mass. The best results were found in case of dense breast tissue, resulting in a sensitivity of 97.52%, specificity of 92.28%, accuracy of 94.82%, false positives rate per image of 0.38 and free-curve receiver operating characteristic of 0.98.