CURVATURE AND SHAPE ANALYSIS FOR THE DETECTION OF SPICULATED MASSES IN BREAST ULTRASOUND IMAGES

Detection and classification of spiculated masses in ultrasound images is still a challenge due to the interference of speckle noise and fuzziness of boundaries. Ultrasound (US) is an important adjunct to mammography in breast cancer detection as it doubles the rate of detection in dense breasts do a dynamic analysis of moving structures in breast. This paper presents technique to detect spiculations and boundary of spiculated masses in breast ultrasound images. In the proposed method, ultrasound images are preprocessed using Gaussian smoothing to remove additive noise and anisotropic diffusion filters to remove multiplicative noise (speckle noise). Active contour method has been used to extract a closed contour of filtered image which is the boundary of the spiculated mass. Spiculations which make breast mass unstructured or irregular are marked by measuring the angle of curvature of each pixel at the boundary of mass. To classify the breast mass as malignant or benign we have used the structure of mass in accordance with spiculations and elliptical shape. We have used receiver operating characteristic curve (ROC) to evaluate the performance. We have validated the proposed algorithm on 100 sub images(40 spiculated and 60 non spiculated) and results shows 90.5% of sensitivity with 0.87 Area Under Curve. Proposed techniques were compared and contrasted with the existing methods and result demonstrates that proposed algorithm has successfully detected spiculated mass ROI candidates in breast ultrasound images.