Robust information clustering incorporating spatial information for breast mass detection in digitized mammograms

In this paper, we investigate a robust information clustering (RIC) algorithm incorporating spatial information for breast mass detection in digitized mammograms. The detection system employs RIC algorithm based on the raw region of interest (ROI) extracted from global mammogram by two steps of adaptive thresholding. Pixels on the fuzzy margin of a mass and noisy data were identified by RIC through the minimax optimization of mutual information. The memberships of the identified pixels (outliers) were recalculated by incorporating spatial distance information that takes into account of the influence of a neighborhood of 3 × 3 window. The algorithm is robust in the sense that both peak and valley of image intensity histogram are estimated and the pixels corresponding to valley in the histogram are clustered adaptively to image content. The purpose of the detection system is to locate the suspicious regions of mass candidates in the mammograms which will be further examined by other diagnostic techniques or by radiologists. The proposed method has been verified with 60 mammograms in the MiniMIAS database. The experimental results show that the detection system has a sensitivity of 90.7% at 2.57 false positives (FPs) per image.