Automatic Application Watershed in Early Detection and Classification Masses in Mammography Image using Machine Learning Methods

Radiologists use mammogram images for the diagnosis of breast cancer. However, interpreting these images remains challenging depending on the type of breast, especially on dense breasts. Dense breasts may contain abnormal structures similar to normal breast tissue and could lead to a high rate of false positives and false positives negatives. We present an efficient computer-aided diagnostic system for detecting and classifying breast masses. After removing noise and artifacts from the images using 2D median filtering, mathematical morphology and pectoral muscle removal by Hough's algorithm, the resulting image is used for breast mass segmentation using the watershed algorithm. After the segmentation, the system extracts several data by the wavelet transform and the co-occurrence matrix (GLCM) to finally lead to a classification as malign or benign mass via the Support Vector Machine (SVM) classifier. This method was applied to 48 Medio-Lateral Oblique (MLO) images from the image base (mini-MIAS). The algorithm showed a 87.5% classification rate, 92.59% sensitivity, and a specificity of 93.94%.