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

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 reg...