Abstract
The level set is a classical image segmentation method, but during the evolution of the level set, it can produce evolutionary problems such as local spikes and deep valleys, or overly flat regions, making the iterative process of final segmentation unstable and segmentation results inaccurate. In order to ensure the stability and validity of the level set evolution during the evolution process, the level set function must be periodically initialized so that the level set is always kept as a signed distance function. We construct a new distance regularization potential function based on logarithmic and power function and give a specific analysis. During the evolution process, the level set function always approximates the signed distance function, which is stable and efficient for level set image segmentation. Experimental analyses are conducted to compare the segmentation performance of various distance regularization potential functions when combining with the classical Chan Vese model.
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Acknowledgements
The authors would like to express their thanks to the referees for their valuable suggestions. This work was supported in part by the grant of the National Natural Science Foundation of China, Nos. 61672204 and 61806068, in part by the grant of Anhui Provincial Natural Science Foundation, Nos. 1908085MF184, 1908085QF285, in part by the Key Research Plan of Anhui Province, No. 201904d07020002.
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Zou, L., Huang, QJ., Wu, ZZ., Song, LT., Wang, XF. (2021). A Robust Distance Regularized Potential Function for Level Set Image Segmentation. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_45
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DOI: https://doi.org/10.1007/978-3-030-84522-3_45
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