Abstract
In this paper, an efficient hybrid level set (HLS) model is proposed for segmenting the images with intensity inhomogeneity, which is a difficult problem for traditional region-based level set methods. The total energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-initialization step widely adopted in traditional level set methods can be avoided by introducing a penalizing energy. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of the proposed model.
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Wang, XF., Min, H. (2009). A Level Set Based Segmentation Method for Images with Intensity Inhomogeneity. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_72
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DOI: https://doi.org/10.1007/978-3-642-04020-7_72
Publisher Name: Springer, Berlin, Heidelberg
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