Segmentation of Pulmonary Nodules Using Fuzzy Clustering Based on Coefficient of Curvature

Pulmonary nodules are potential manifestation of lung cancer. Accurate segmentation of juxta-vascular nodules and ground glass opacity (GGO) nodules are an important and active area of research in medical image processing. At present, the classical segmentation algorithm of pulmonary nodules can not accurately obtain the boundary information of pulmonary nodules. In order to solve the problem, a new segmentation algorithm of pulmonary nodules using Fuzzy clustering based on coefficient of curvature is proposed in this paper. Because the coefficient of curvature can effectively distinguish between pulmonary nodules and surrounding structures, and Fuzzy clustering algorithm is adopted, the boundary information of pulmonary nodules can be accurately obtained. In order to obtain initial segmentation results of pulmonary nodules, the expectation maximization algorithm is adopted. Experimental results show that the proposed algorithm can accurately obtain the boundary information of juxta-vascular nodules and GGO nodules.