Linguistics

Histogram

Philosophy

Point distribution model

Statistics

Computer science

Sørensen–Dice coefficient

Vector flow

Image segmentation

Segmentation

Point cloud

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Feature (linguistics)

Artificial intelligence

Geometry

Point (geometry)

Standard deviation

Computer vision

Iterative closest point

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.

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