title: A novel level set model with automated initialization and controlling parameters for medical image segmentation.
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.
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China.
authors.forename: Qingyi
authors.initials: Q
authors.lastname: Liu
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China. Electronic address: jiangmingyan@sdu.edu.cn.
authors.forename: Mingyan
authors.initials: M
authors.lastname: Jiang
authors.affiliation: College of Electronic, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, China.
authors.forename: Peirui
authors.initials: P
authors.lastname: Bai
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China.
authors.forename: Guang
authors.initials: G
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Adaptive mean shift clustering; Automated initialization; Level set method; Medical image segmentation; Reaction diffusion method