Fully automated gradient based breast boundary detection for digitized X-ray mammograms

Abstract

Accurate segmentation of the breast from digital mammograms is an important pre-processing step for computerized breast cancer detection. In this study, we propose a fully automated segmentation method. Noise on the acquired mammogram is reduced by median filtering; multidirectional scanning is then applied to the resultant image using a moving window 15×1 in size. The border pixels are detected using the intensity value and maximum gradient value of the window. The breast boundary is identified from the detected pixels filtered using an averaging filter. The segmentation accuracy on a dataset of 84 mammograms from the MIAS database is 99%.