Active contour model based on local and global intensity information for medical image segmentation

NEUROCOMPUTING

卷:186

Article

Zhou, Sanping¹  Wang, Jinjun¹  Zhang, Shun¹  Liang, Yudong¹  Gong, Yihong¹ 

[1] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian Ning West Rd 28, Xian 710049, Shaanxi, Peoples R China

关键词: Medical image segmentation;    Intensity inhomogeneity;    Level set method;    Maximum a posteriori probability (MAP);    Active contour model;   

DOI  :  10.1016/j.neucom.2015.12.073

来源: Elsevier

【 摘 要 】

This paper proposes a novel region-based active contour model in the level set formulation for medical image segmentation. We define a unified fitting energy framework based on Gaussian probability distributions to obtain the maximum a posteriori probability (MAP) estimation. The energy term consists of a global energy term to characterize the fitting of global Gaussian distribution according to the intensities inside and outside the evolving curve, as well as a local energy term to characterize the fitting of local Gaussian distribution based on the local intensity information. In the resulting contour evolution that minimizes the associated energy, the global energy term accelerates the evolution of the evolving curve far away from the objects, while the local energy term guides the evolving curve near the objects to stop on the boundaries. In addition, a weighting function between the local energy term and the global energy term is proposed by using the local and global variances information, which enables the model to select the weights adaptively in segmenting images with intensity inhomogeneity. Extensive experiments on both synthetic and real medical images are provided to evaluate our method, show significant improvements on both efficiency and accuracy, as compared with the popular methods. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free