Abstract
The region-scalable fitting energy (RSF) model is one of the classical active contour models, it performs well while dealing with the images with intensity inhomogeneity. However, complex vessels with more branches and different sizes make it disabled for image segmentation. In order to overcome the difficulties in complex vessels segmentation, we propose an improved region-scalable fitting energy. It consists of area term, length term, and penalty term. Due to the length term and penalty term both smoothing the zero level contour, they may prevent the level set function from evolving. Therefore, we use the arctangent function to reduce the impact of penalty term and length term to further evolve. And we modify the area term to denoise. The experiments show that the proposed method can have a better segmentation performance while dealing with complex vessels with intensity inhomogeneity. The proposed method is less sensitive to initial contour and robust to illumination.
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Hou, J., Yin, Q., Wu, P., Lu, M. (2020). Vessel Segmentation Based on Region-Scalable Fitting Energy. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_51
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DOI: https://doi.org/10.1007/978-3-030-32591-6_51
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