A novel approach for breast skin-line estimation in mammograms

Breast skin-line extraction in mammograms is useful to radiologists and image processing scientists as it aids in the analysis of mammograms. Prior detection and delineation of the skin-line can reduce the effects of background noise and artifacts on procedures for image enhancement and detection of signs of breast cancer. The proposed system works in the following way. An initial estimate of the skin-line is computed using a combination of adaptive thresholding (T. Ojala et al., 2001) and connected-component analysis. The novelty of our dependency approach for skin-line estimation lies in the way we compute the Euclidean distance constraints between the initial skin-line and the stroma edge computed via bimodal histogram analysis. Because the Euclidean distance from the edge of the stroma to the actual skin-line is usually uniform, these constraints are propagated to estimate the upper or lower skin-line portions. The selection of the constrained region is based on a greedy algorithm, which is also a new component in our system. We evaluated the performance of our skin-line estimation algorithm by comparing the estimated boundary with respect to the ground-truth boundary drawn by an expert radiologist. We used polyline distance metrics for error measurement (Y. Sun et al, 2005). As part of our protocol, we compared our dependency approach methodology with a deformable model strategy (see Ferrari et al. (2004)). On a dataset of 82 images from the MIAS database (J. Suckling et al., 1994), using our dependency approach, the polyline distance error metric yielded a mean error of 3.28 pixels with a standard deviation of 2.17 pixels. In comparison, the deformable model strategy (R.J. Ferrari et al., 2004) yielded a mean error of 4.92 pixels and a standard deviation of 1.91 pixels. The results obtained have been verified by radiologists, who have indicated that the improvement obtained is clinically significant.