GPU-accelerated lung CT segmentation based on level sets and texture analysis.

ABSTRACT: This paper presents a novel semi-automatic method for lung segmentation in thoracic CT datasets. The fully three-dimensional algorithm is based on a level set representation of an active surface and integrates texture features to improve its robustness. The method's performance is enhanced by the graphics processing unit (GPU) acceleration. The segmentation process starts with a manual initialisation of 2D contours on a few representative slices of the analysed volume. Next, the starting regions for the active surface are generated according to the probability maps of texture features. The active surface is then evolved to give the final segmentation result. The recent implementation employs features based on grey-level co-occurrence matrices and Gabor filters. The algorithm was evaluated

SUBMITTER: Reska D 

PROVIDER: S-EPMC10792028 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature