Active contour evolved by joint probability classification on Riemannian manifold. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Ge, Qi; Shen, Fumin; Jing, Xiao-Yuan; Wu, Fei; Xie, Shi-Peng; Yue, Dong; Li, Hai-Bo
• Is Part Of: Signal, image and video processing. Volume 10:Issue 7(2016)
• Page Start: 1257
• Page End: 1264
• Publication Date: 2016-10
• Subjects: Image segmentation -- Joint probability classification -- Active contour -- Nonparametric distribution -- Riemannian manifold; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0891-8
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract In this paper, we present an active contour model for image segmentation based on a nonparametric distribution metric without any intensity a priori of the image. A novel nonparametric distance metric, which is called joint probability classification, is established to drive the active contour avoiding the instability induced by multimodal intensity distribution. Considering an image as a Riemannian manifold with spatial and intensity information, the contour evolution is performed on the image manifold by embedding geometric image feature into the active contour model. The experimental results on medical and texture images demonstrate the advantages of the proposed method.