Mammographic Mass Segmentation Using Fuzzy C-means and Decision Trees.

Breast cancer is the second most frequent cancer among Cuban women. The mammographic image processing constitutes a challenge due to the breast anatomy diversity and its low–contrast that doesn’t allow a good border definition and anomalies visualization. The aim of this research is the mass anomaly segmentation and classification. Two algorithms are presented: (i) an efficient mass segmentation approach based on a Fuzzy C–means modification using image histogram, and (ii) a method for classifying regions of interest corresponding to masses, based on a binary decision tree. The novelty on classifier training consists in using co-occurrence matrices of the region of interest’s radial image. The images are pre-processed with re-scaling, CLAHE and homogeneity filters. Mammograms pre-processing importance and the fuzzy method’s effectiveness were shown by the experiments. In the classification step, we obtained 90% sensitivity and 72% specificity, while reducing false positives we reached 87% sensitivity and 88% specificity.