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A Hybride Active Contour Model Driven by Global and Local Image Information

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Abstract

In this paper, a novel active contour model based on hybrid image fitting energy which utilizes both global and local image information is proposed. Two fitting images are constructed to approximate the original image and the square of the original image. Both global and local image information are incorporated into these two fitting images. Based on these two fitting images, a hybrid image fitting energy, which is then minimized in a variational level set framework to guide the evolving contours to the desired boundaries. The proposed approach is validated by experiments on both synthetic and real images. The experiments demonstrate that the proposed model is more efficient and robust for segmenting different kinds of images compared with several typical active contour models.

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Notes

  1. The matlab source code of the C–V, the RSF, the LIF and the LGIF algorithms can be found from http://www.engr.uconn.edu/~cmli/, http://www.kaihuazhang.net/ and https://www.unc.edu/~liwa/.

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Acknowledgements

The author (Hairong Liu) was supported by Natural Science Foundation of Jiangsu Province (BK20140965) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China (14KJB110010). The author (Yu Xing) was supported by the Natural Science Foundation for Youths of Jiangsu Province (BK20171072), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (17KJB110007) and the Open Project of Jiangsu Key Laboratory of Financial Engineering (NSK2015-15).

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Authors and Affiliations

  1. School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310027, China

    Xu Li

  2. School of Science, Nanjing Forestry University, Nanjing, 210037, China

    Hairong Liu

  3. School of Finance, Nanjing Audit University, Nanjing, 211815, China

    Yu Xing

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  1. Xu Li
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  2. Hairong Liu
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  3. Yu Xing
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Correspondence to Xu Li.

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Li, X., Liu, H. & Xing, Y. A Hybride Active Contour Model Driven by Global and Local Image Information. Neural Process Lett 50, 989–1003 (2019). https://doi.org/10.1007/s11063-019-10004-0

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