Proposal of Fast Implicit Level Set Scheme for Medical Image Segmentation Using the Chan and Vese Model
People living in the information age, are more and more attention to their lives. It is also said, social life is more important in present and future. The social life contains three fields. In this paper, we propose a new model for active contours to detect objects in a given medical image, in order to facilitate people to have medical treatment. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semiimplicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, our model uses the level set which are corresponding to ordinary differential equation (ODE). Our model has more improved characteristics than traditional models, such as: less sensibility of noise; unnecessary of re-initialization and high speed by the simplified ordinary differential function. Finally, we validate the proposed model by numerical synthetic and real images. The experimental results demonstrate that our model is at least two times more efficient than the widely used methods.
image segmentation level sets curvature ordinary differential equation denoising edge detection AOS scheme active contours social life
Huimin Lu Seiichi Serikawa Yujie Li Lifeng Zhang Shiyuan Yang Xuelong Hu
Department of Electrical Engineering and Electronics, Kyushu Institute of Technology,1-1 Sensui-Cho, Department of Electrical Engineering and Electronics, Kyushu Institute of Technology,1-1 Sensui-Cho, School of Information Engineering, Yangzhou University, 196 West Huayang Road, Hanjiang,Yangzhou 225
国际会议
湘潭
英文
695-699
2011-07-19（万方平台首次上网日期，不代表论文的发表时间）