Figure 2.

The step-by-step procedures for estimating the air (lung region) in each subject to generate the chest model for coregistration with the template. (a) The original breast MR image with background noise (e.g., the ghosting artifact coming from the heart motion outside the breast). (b) This outside noise is removed by using Otsu's automated thresholding algorithm followed by morphological operations of erosion and open. (c) An initial k-means clustering with (k = 2) is applied to locate the dark region, which may also include fibroglandular tissue and chest wall muscle. (d) Within this contour, k-means clustering (k = 10) is applied again to identify the area with the lowest intensity (cluster#1). (e) The largest area of cluster#1 in the left side and the right side is detected and the center of mass in each side is used as the seed point for region growing. (f) Based on the center of the initial air region (the highlighted dot), an ellipse that has the best fit to the initial air region is then generated. For the chest region above the center, the boundary of the initial air region and the estimated ellipse is averaged and smoothed to obtain a smooth boundary. For the chest region below the center, the boundary estimated by region growing is directly used. (g) The resulted contour is indicated by circles. Bezier curve fitting is then applied to the circles to obtain a smooth contour of the estimated chest region. (h) Finally, the SNC image is generated (cropped at 8 mm above the chest model contour, and then normalized into 10 clusters and smoothed).