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Active contour model combining region and edge information

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Abstract

A novel active contour model is proposed by combining region and edge information. Its level set formulation consists of the edge-related term, the region-based term and the regularization term. The edge-related term is derived from the image gradient, and facilitates the contours evolving into object boundaries. The region-based term is constructed using both local and global statistical information, and related to the direction and velocity of the contour propagation. The last term ensures stable evolution of the contours. Finally, a Gaussian convolution is used to regularize the level set function. In addition, a new quantitative metric named modified root mean squared error is defined, which can be used to evaluate the final contour more accurately. Experimental results show that the proposed method is efficient and robust, and can segment homogenous images and inhomogenous images with the initial contour being set freely.

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Authors and Affiliations

  1. College of Information Science & Technology, Beijing Normal University, Beijing, 100875, China

    Yun Tian, Fuqing Duan, Mingquan Zhou & Zhongke Wu

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  1. Yun Tian
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  2. Fuqing Duan
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  3. Mingquan Zhou
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  4. Zhongke Wu
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Correspondence to Yun Tian.

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Tian, Y., Duan, F., Zhou, M. et al. Active contour model combining region and edge information. Machine Vision and Applications 24, 47–61 (2013). https://doi.org/10.1007/s00138-011-0363-7

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  • DOI: https://doi.org/10.1007/s00138-011-0363-7

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