Fully automatic bone segmentation through contrast enhanced torso CT datasets

In analysis and diagnosis of torso diseased organs, bone elimination from contrast enhanced CT datasets is of high importance. Yet, bone elimination from contrast enhanced CT datasets is obtained using two phases; native and desired contrast enhanced phases. This elimination is performed by segmenting bones from native phase, registering the contrast enhanced phase of the dataset with its native phase, then subtracting overlapped voxels of segmented bones on the contrast enhanced dataset. This technique for subtraction is time consuming and error prone depending on registration results. In this paper, we propose a fully automatic method to segment and classify bone though any single contrast enhanced torso CT images as well as native phase. This bone segmentation is achieved in four steps: coarse bone candidates are segmented by automatically adapted threshold, cortical bone is classified using shape and intensity, spongy bone is classified by solid angle, abdominal bone and pelvis are separated based on smoothing histogram, and. The method was examined using 64 datasets in different phases of contrast enhanced CT dataset. Bone is segmented and classified in a total 45360 slices. For evaluation, results of segmenting bones through arterial, portal and equilibrium phases were compared to results of native phase 94%, 98%, 97% respectively. These results demonstrate that the method is promising in segmenting torso bone from any single phase of CT and may have utility in clinical use. The method is capable of segmenting bone through thoracic and abdominal images separately, and to classify torso bone into ribs, spine and pelvis.