title: [Image segmentation in tumor CT based on the improved C-V model].
abstract: Aiming at the shortcomings of slow convergence and inaccuracy segmentation in non-homogeneous images, improvements were made on the traditional C-V model in two aspects. Firstly, using a novel model based on local gradient, the initial contour of the C-V model was quickly moved near the target border, greatly reducing the evolution time. Secondly, combining the characteristics of GVF model from two directions to the target border, an adaptive velocity reconciling item was added for velocity equation of the C-V model to make the model converge to the true border. The segmentation experiments for liver tumors in CT showed that the proposed method could be effective.
authors.affiliation: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China. zjghappy@126.com
authors.forename: Jianguo
authors.initials: J
authors.lastname: Zhang
authors.forename: Rongguo
authors.initials: R
authors.lastname: Zhang
authors.forename: Fei
authors.initials: F
authors.lastname: Xue
authors.forename: Kun
authors.initials: K
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods*