An efficient local Chan–Vese model for image segmentation

• Published in: Pattern recognition Vol. 43; no. 3; pp. 603 - 618
• Main Authors: Wang, Xiao-Feng, Huang, De-Shuang, Xu, Huan
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2010; Elsevier
• Subjects: Applied sciences; Exact sciences and technology; Extended structure tensor; Image processing; Image segmentation; Information, signal and communications theory; Intensity inhomogeneity; Level set method; Local Chan–Vese model; Signal processing; Telecommunications and information theory; Local Chan–Vese model; Image segmentation; Intensity inhomogeneity; Extended structure tensor; Level set method; Performance evaluation; Local Chan-Vese model; Texture analysis; Image processing; Transmission protocol; Iterative method; Texture; Heterogeneity; Routing protocols; Robustness; Localization; Content analysis
• DOI: 10.1016/j.patcog.2009.08.002
• ISSN: 0031-3203

In this paper, a new local Chan–Vese (LCV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, local statistical function and level set method. The energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-initialization step widely adopted in traditional level set methods can be avoided by introducing a new penalizing energy. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented which is based on the length change of evolving curve. Particularly, we proposed constructing an extended structure tensor (EST) by adding the intensity information into the classical structure tensor for texture image segmentation. It can be found that by combining the EST with our LCV model, the texture image can be efficiently segmented no matter whether it presents intensity inhomogeneity or not. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with the well-known Chan–Vese (CV) model and recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.