Probabilistic boosting trees for automatic bone removal from CT angiography images
Arne Militzer, Fernando Vega-Higuera · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
In CT angiography images, osseous structures occluding vessels pose difficulties for physicians during diagnosis. Simple thresholding techniques for removing bones fail due to overlapping CT values of vessels filled with contrast agent and osseous tissue, while manual delineation is slow and tedious. Thus, we propose to automatically segment bones using a trainable classifier to label image patches as bone or background. The image features provided to the classifier are based on grey value statistics and gradients. In contrast to most existing methods, osseous tissue segmentation in our algorithm works without any prior knowledge of the body region depicted in the image. This is achieved by using a probabilistic boosting tree, which is capable of automatically decomposing the input space. The whole system works by partitioning the image using a watershed transform, classifying image regions as bone or background and refining the result by means of a graph-based procedure. Additionally, an intuitive way of manually refining the segmentation result is incorporated. The system was evaluated on 15 CTA datasets acquired from various body regions, showing an average correct recognition of bone regions of 80% at a false positive rate of 0.025% of the background voxels.