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Color Cell Image Segmentation Based on Chan-Vese Model for Vector-Valued Images

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DOI: 10.4236/jsea.2013.610066    2,737 Downloads   4,096 Views   Citations

ABSTRACT

In this paper, we propose a color cell image segmentation method based on the modified Chan-Vese model for vectorvalued images. In this method, both the cell nuclei and cytoplasm can be served simultaneously from the color cervical cell image. Color image could be regarded as vector-valued images because there are three channels, red, green and blue in color image. In the proposed color cell image segmentation method, to segment the cell nuclei and cytoplasm precisely in color cell image, we should use the coarse-fine segmentation which combined the auto dual-threshold method to separate the single cell connection region from the original image, and the modified C-V model for vectorvalued images which use two independent level set functions to separate the cell nuclei and cytoplasm from the cell body. From the result we can see that by using the proposed method we can get the nuclei and cytoplasm region more accurately than traditional model.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

J. Fan, S. Li and C. Zhang, "Color Cell Image Segmentation Based on Chan-Vese Model for Vector-Valued Images," Journal of Software Engineering and Applications, Vol. 6 No. 10, 2013, pp. 554-558. doi: 10.4236/jsea.2013.610066.

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