Active contour evolved by joint probability classification on Riemannian manifold. Issue 7 (October 2016) - Ge, Qi; Shen, Fumin; Jing, Xiao-Yuan; Wu, Fei; Xie, Shi-Peng; Yue, Dong; Li, Hai-Bo
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.