Automated Approach for Qualitative Assessment of Breast Density and Lesion Feature Extraction for Early Detection of Breast Cancer

Breast cancer is one of the leading causes of fatality in women. Mammogram is the effectual modality for early detection of breast cancer. Increased mammographic breast density is a moderate independent risk factor for breast cancer, Radiologists have estimated breast density using four broad categories (BI-RADS) swearing on visual assessment of mammograms. But if we can measure breast density quantitatively, we can provide most accurate and a reliable density measures. Breast density and Lesion feature extraction plays important role in determining cancer risk. Breast contour helps to find the position of the nipple, as its position is important for registration of left and right breasts, to detect bilateral asymmetry. The shape of the mass border helps radiologists to judge whether mass is malignant or benign.. Novel algorithms are designed for 1) Breast Density Estimation 2) Breast border 3) Segmentation of mass and for deriving the mass border,4) Extraction of haralick features[15] from the mass. These features help to further investigate in a clinical evaluation for classification to detect the cancer in early stages. We processed fourteen mammograms for breast border extraction, of which we segmented and calculated features for six patients, who have masses. Keywords—Breast density, Mass, Malignant, Benign, Feature Extraction.