Breast cancer detection using image processing techniques

Abstract

We describe the use of segmentation with fuzzy models and classification by the crisp k-nearest neighbor (k-nn) algorithm for assisting breast cancer detection in digital mammograms. Our research utilizes images from the Digital Database for Screening Mammography (DDSM). We show that supervised and unsupervised methods of segmentation, such as k-nn and fuzzy c-means (FCM), in digital mammograms will have high misclassification rates when only intensity is used as the discriminating feature. Adding window means and standard deviations to the feature suite (visually) improves segmentations produced by the k-nn rule. While our results are encouraging, other methods are needed to detect smaller pathologies such as microcalcifications.