Automated detection of tumors in mammograms using two segments for classification

A spread pattern of a tumor in medical images is an important factor for classification of the tumor. The spread pattern is generally not considered when we use only one segment for classification. In order to include the spread pattern for tumor analysis, we propose an approach for classification of tumors in mammograms using two segments for a mass. The proposed approach is performed in two stages. In the first stage, the system separates segments of the image that may correspond to tumors using a combination of morphological operations and a region growing technique. In the second stage, segmented regions are classified as normal, benign, or malignant tissues based on different measurements. The measurements pertain to shape, intensity variation around the mass, as well as the spread pattern. Experimental results with mammogram images of the MIAS database show reasonable improvements in correct detection of possible tumors, compared to other approaches.