A novel level set model with automated initialization and controlling parameters for medical image segmentation. (March 2016)

• Record Type: Journal Article
• Authors: Liu, Qingyi; Jiang, Mingyan; Bai, Peirui; Yang, Guang
• Is Part Of: Computerized medical imaging and graphics. Volume 48(2016)
• Page Start: 21
• Page End: 29
• Publication Date: 2016-03
• Subjects: Medical image segmentation -- Adaptive mean shift clustering -- Level set method -- Automated initialization -- Reaction diffusion method; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2015.12.005
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Only one parameter needs to be changed manually for different images in our model. We propose a novel adaptive mean shift clustering method to guide the evolution of level set. The results of clustering can automatically generate an initial contour of level set evolution. The controlling parameters of level set evolution can be automatically estimated. We use reaction diffusion method in our model to avoid re-initialization. Abstract: In this paper, a level set model without the need of generating initial contour and setting controlling parameters manually is proposed for medical image segmentation. The contribution of this paper is mainly manifested in three points. First, we propose a novel adaptive mean shift clustering method based on global image information to guide the evolution of level set. By simple threshold processing, the results of mean shift clustering can automatically and speedily generate an initial contour of level set evolution. Second, we devise several new functions to estimate the controlling parameters of the level set evolution based on the clustering results and image characteristics. Third, the reaction diffusion method is adopted to supersede the distance regularization term of RSF-level set model, which can improve the accuracy and speed of segmentation effectively with less manual intervention. Experimental results demonstrate the performance and efficiency of the proposed model for medical image segmentation.