Color Image Segmentation Using Level Set Method With Initialization Mask in Multiple Color Spaces

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Zhang Yongqin 1,* Chen Hui 2 Wang Ling 3 Xiao Yongjun 4 Huang Haibo 3

1. School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China;

2. China Information Technology Designing & Consulting Institute Co., LTD, Zhengzhou 450007, China;

3. School of Physics and Electronic Information Engineering, Xiaogan University, Xiaogan 432000, China;

4. School of Electronic Information, Wuhan University, Wuhan 430079, China

* Corresponding author.


Received: 20 May 2011 / Revised: 24 Jun. 2011 / Accepted: 22 Jul. 2011 / Published: 29 Aug. 2011

Index Terms

Image segmentation, color space, level set method, Mumford-Shah framework


The aim of image segmentation in imaging science is to solve the problem of partitioning an image into smaller disjoint homogeneous regions that share similar attributes. The improvement of level set method (LSM) based on Chan-Vese (C-V) model with initialization mask for vector image segmentation in multiple color spaces is studied here. And simultaneously, the final segmentation is completed by a simple labeling scheme. Then the comparative study of the refined C-V model is done in multiple color spaces. The experimental results illustrate that the optimized C-V model leads faster and better segmentation results with robustness to noise and good adaptability in RGB, CIE XYZ, and YCbCr color spaces where the results of test image changes little. But it has made mistakes in HSV and CIE L*a*b* color model. Moreover, these color spaces, i.e. h1h2h3, produce poor segmentation on the reliability and accuracy of a set of test images by performance analysis with evaluation indicators.

Cite This Paper

Zhang Yongqin,Chen Hui,Wang Ling,Xiao Yongjun,Huang Haibo,"Color Image Segmentation Using Level Set Method With Initialization Mask in Multiple Color Spaces", IJEM, vol.1, no.4, pp.70-76, 2011. DOI: 10.5815/ijem.2011.04.11


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