Prostate segmentation in CT data using active shape model built by HoG and non-rigid Iterative Closest Point registration

In the paper a new method for prostate segmentation in computed tomography (CT) data is proposed. In the proposed approach, first, corresponding points of training data sets are found using point clouds generation by Marching Cubes algorithm and non-rigid Iterative Closest Points registration. After that, having the corresponding points available, the statistical model of the prostate is built by the Active Shape Model (ASM). As a feature vector...