Figure 2.

Flowchart of the proposed method. The target image was affine-registered to a probabilistic atlas in terms of the extracted high intensity structures. Specific types of bony structures were then identified by transferring labels from the atlas to the target image based on a Bayesian framework, which incorporated the position information from the atlas and the intensity distribution for each label. After the skin is segmented by a curvature-constrained level set method, the two anterior iliac crests and all visible ribs are selected as landmarks, based on which three coordinates were created. The skin is then colored with RGB values converted by the normalized shortest distances to the biomarkers. Texture analysis followed by a fuzzy c-means procedure was used to estimate a voxel-wise probabilistic membership. An edge map was derived from the membership to guide the level set evolution, while the hard segmentation of muscles from the membership combined with the segmented skin was used to derive the initial start. Ground truth was manually labeled for the abdominal wall to calculate the surface errors of automatic segmentation.