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Active contour evolved by joint probability classification on Riemannian manifold

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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.

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Acknowledgments

This work was supported in part by the Natural Science Foundation Science Foundation of China under Grant (61502244, GZ215022, 61402239, 71301081), the Science Foundation of Jiangsu Province under Grant (BK20150859, BK20130868, BK20130877), the Science Foundation of Jiangsu Province University (15KJB520028), NJUPT Talent Introduction Foundation (NY213007), NJUPT Advanced Institute Open foundation (XJKY14012), China Postdoctoral Science Foundation (2015M580433, 2014M551637), Postdoctoral Science Foundation of Jiangsu Province (1401046C).

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Authors and Affiliations

  1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

    Qi Ge & Shi-Peng Xie

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Fumin Shen

  3. Wuhan University, Wuhan, China

    Xiao-Yuan Jing

  4. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China

    Xiao-Yuan Jing & Fei Wu

  5. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Dong Yue

  6. School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden

    Hai-Bo Li

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  1. Qi Ge
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  2. Fumin Shen
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  3. Xiao-Yuan Jing
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  4. Fei Wu
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  5. Shi-Peng Xie
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  6. Dong Yue
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  7. Hai-Bo Li
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Correspondence to Qi Ge.

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Ge, Q., Shen, F., Jing, XY. et al. Active contour evolved by joint probability classification on Riemannian manifold. SIViP 10, 1257–1264 (2016). https://doi.org/10.1007/s11760-016-0891-8

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  • DOI: https://doi.org/10.1007/s11760-016-0891-8

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