Accurate estimation of the breast skin-line is an important prerequisite for both enhancement and analysis of mammograms for computer-aided detection of breast cancer. In our proposed system, an initial estimate of the skin-line is first computed using a combination of adaptive thresholding and connected-component analysis. This skin-line is susceptible to errors in the top and bottom portions of the breast region. Using the observation that the Euclidean distance from the edge of the stroma to the actual skin-line is usually uniform, we develop a novel dependency approach for estimating the skin-line boundary of the breast. In the proposed dependency approach, the constraints are first developed between the stroma edge and the initial skin-line boundary using the Euclidean distance. These constraints are then propagated to estimate the upper and lower skin-line portions. 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 adapted a metrics for error measurement: the polyline distance measure (PDM). As part of our protocol, we compared the results of our dependency approach methodology with those of a deformable model strategy (proposed by Ferrari et al. in Med Biol Eng Comput 42(2):210–208, 2004). On a dataset of 82 images from the MIAS database, the dependency approach yielded a mean error (μ) of 3.28 pixels with a standard deviation (σ) of 2.17 pixels using the PDM. In comparison, the deformable model strategy (Ferrari et al. in Med Biol Eng Comput 42(2):210–208, 2004) yielded μ = 4.92 pixels with σ = 1.91 pixels. The improvement is statistically significant. The results are clinically relevant, according to the radiologists who evaluated the results.
Breast skin-line Dependency approach Stroma edge Greedy algorithm Polyline distance Active contours MIAS database