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

Abstract

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 histogram of image gradient (HoG) is utilized. Finally, the ASM is used once more for the target prostate segmentation: the statistical prostate model is fitted to the CT data. Efficiency of the proposed segmentation algorithm is validated using the Dice coefficient reaching the value 0.807 with standard deviation 0.045. The method can cope with data anisotropy.