title: Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information.
abstract: By using entropy and local neighborhood information, we present in this study a robust adaptive Gaussian regularizing Chan-Vese (CV) model to segment the myocardium from magnetic resonance images with intensity inhomogeneity. By utilizing the circular Hough transformation (CHT) our model is able to detect epicardial and endocardial contours of the left ventricle (LV) as circles automatically, and the circles are used as the initialization. In the cost functional of our model, the interior and exterior energies are weighted by the entropy to improve the robustness of the evolving curve. Local neighborhood information is used to evolve the level set function to reduce the impact of the heterogeneity inside the regions and to improve the segmentation accuracy. An adaptive window is utilized to reduce the sensitivity to initialization. The Gaussian kernel is used to regularize the level set function, which can not only ensure the smoothness and stability of the level set function, but also eliminate the traditional Euclidean length term and re-initialization. Extensive validation of the proposed method on patient data demonstrates its superior performance over other state-of-the-art methods.
authors: Zhengzhou University of Light Industry, Zhengzhou 450002, China.
authors: Qian
authors: Q
authors: Zheng
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors: Zhentai
authors: Z
authors: Lu
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors: Minghui
authors: M
authors: Zhang
authors: University of Electronic Science and Technology of China, Chengdu 611731, China.
authors: Lin
authors: L
authors: Xu
authors: Zhengzhou University of Light Industry, Zhengzhou 450002, China.
authors: Huan
authors: H
authors: Ma
authors: Zhengzhou University of Light Industry, Zhengzhou 450002, China.
authors: Shengli
authors: S
authors: Song
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors: Qianjin
authors: Q
authors: Feng
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors: Yanqiu
authors: Y
authors: Feng
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors: Wufan
authors: W
authors: Chen
authors: Cardiovascular Sciences Research Centre, St George's, University of London, London SW17 0RE, United Kingdom; Biomedical Research Unit, Royal Brompton Hospital and Imperial College London, London SW7 2AZ, United Kingdom.
authors: Taigang
authors: T
authors: He
mesh_terms: D000465:Algorithms; D003326:Coronary Circulation; D006352:Heart Ventricles; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008962:Models, Theoretical; D009206:Myocardium