Computerized detection of breast cancer in digital mammograms

Abstract

In this paper, authors propose an efficient system for the segmentation of lesions in digital mammograms. The proposed detection system comprises three steps. In the first step, efficient pre-processing technique is developed using top-hat morphological transform, wavelet transform, and morphological opening–closing reconstruction filter followed by median filter. In the second step, threshold selection procedure is developed using a combination of Fuzzy C-means (FCM) clustering and Otsu global thresholding method. Finally, computed threshold is used to binarize the pre-processed image thereby yielding the suspicious mass regions from it. The free response operating characteristics (FROC) curve is used to evaluate the performance of the proposed method. Proposed system achieved the sensitivity of 94% at the rate of 1.30 false positives per image.

Keywords:

• Image processing
• mammograms
• segmentation
• breast cancer
• wavelet transforms
• fuzzy C-means