Abstract
Image segmentation based on active contour model has been widely used in recent years. However, in real application, image segmentation results are always leaded to corrupt because of partial missing of target boundaries or some misleading factors. In order to solve the issue, Zhou proposed a model called active contour with group similarity. But Zhou’s model is purely based on global information of image and cannot deal with image segmentation with intensity inhomogeneity. In order to optimize Zhou’s model and make it more robust, we construct a new energy function which combines global feature with local feature of image. The local feature can solve the issue of image segmentation with intensity inhomogeneity. And the global feature can make our model less sensitive to the initial position of the curve or noise. The experiment results have proved that, our method can achieve satisfying segmentation results on image segmentation with intensity inhomogeneity and show less sensitive to the initial position of the curve.
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Acknowledgement
This work was supported by the National Key Technology Support Program of China (No. 2015BAF04B00), The Fundamental Research Funds for the central Universities, and the Shanghai Innovation Action Project of Science and Technology (15DZ1101202).
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Chen, S., Zhao, X., Liu, X., Chen, Y. (2017). A More Robust Active Contour Model with Group Similarity. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_38
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DOI: https://doi.org/10.1007/978-3-319-63309-1_38
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