Calibrating level set approach by granular computing in computed tomography abdominal organs segmentation

• Published in: Applied soft computing 2016-12, Vol.49, p.887-900
• Main Authors: Badura, Pawel, Wieclawek, Wojciech
• Format: Article
• Language: English
• Subjects: Abdominal organs; Granular computing; Image fuzzification; Image segmentation; Level sets
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2016.09.028

[Display omitted] •Proposed hybrid methodology is a coarse to fine approach to abdominal organs segmentation.•The evolving organ contour is initialized by means of information granule.•The image is fuzzified using Gaussian membership function.•The granule-adjusted hybrid level set approach yields final segmentation.•The approach effectiveness was confirmed based on 20 CT studies and 4 types of organs. Hybrid methodology that combines granular computing with a level set approach for image segmentation is introduced in this paper. The goal of the study is to provide an adjustable semi-automated method for 3D segmentation of abdominal organs in computed tomography studies. Based on initial guidance in terms of models or seed points the information granule is formulated for each anatomical structure under consideration. The granulation leads via spatial volume resampling and granule-driven image intensity fuzzification to the final segmentation stage employing a hybrid level set approach. Depending on the organ intensity and shape the segmentation runs with selected parameters. The algorithm has been evaluated by 20 computed tomography studies and four various structures delineated by an expert. The experimental part presents a comprehensive analysis of subsequent stages of the algorithm. The voxel-wise segmentation sensitivities reached 94.4% for the right kidney, 93.8% for the left kidney, 94.0% for the spleen, and 94.0% for the liver, with the Dice index at 93.7%, 94.2%, 91.0%, and 92.9%, respectively.