Active contour evolved by joint probability classification on Riemannian manifold

作者：Ge, Qi^*; Shen, Fumin; Jing, Xiao-Yuan; Wu, Fei; Xie, Shi-Peng; Yue, Dong; Li, Hai-Bo

来源：Signal, Image and Video Processing, 2016, 10(7): 1257-1264.

DOI：10.1007/s11760-016-0891-8

• Image segmentation
• Joint probability classification
• Active contour
• Nonparametric distribution
• Riemannian manifold

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.

• 出版日期2016-10
• 单位电子科技大学; 南京邮电大学; 武汉大学