10 April 2018 A robust and fast active contour model for image segmentation with intensity inhomogeneity
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151V (2018) https://doi.org/10.1117/12.2302934
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
In this paper, a robust and fast active contour model is proposed for image segmentation in the presence of intensity inhomogeneity. By introducing the local image intensities fitting functions before the evolution of curve, the proposed model can effectively segment images with intensity inhomogeneity. And the computation cost is low because the fitting functions do not need to be updated in each iteration. Experiments have shown that the proposed model has a higher segmentation efficiency compared to some well-known active contour models based on local region fitting energy. In addition, the proposed model is robust to initialization, which allows the initial level set function to be a small constant function.
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Keyan Ding, Keyan Ding, Guirong Weng, Guirong Weng, "A robust and fast active contour model for image segmentation with intensity inhomogeneity", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151V (10 April 2018); doi: 10.1117/12.2302934; https://doi.org/10.1117/12.2302934
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