Hybrid level set method based on image diffusion

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• 作者：Xiao-Feng Wang ; ^{xfwang@iim.ac.cn}Author Vitae ; Le ZouAuthor Vitae ; Li-Xiang XuAuthor Vitae ; Gang LvAuthor Vitae ; Chao TangAuthor Vitae
• 关键词：Image segmentation ; Level set ; Hybrid method ; Nonlinear diffusion ; Homomorphic unsharp masking
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：8 March 2017
• 年：2017
• 卷：228
• 期：Complete
• 页码：53-64
• 全文大小：2069 K
• 卷排序：228

文摘

In this paper, a new hybrid diffusion-based level set method is proposed to efficiently address the complex image segmentation problem. Different from the traditional methods, the proposed method is performed on image diffusion space rather than intensity space. Firstly, the nonlinear diffusion based on total variation flow and additive operator splitting scheme is performed on the original intensity image to obtain the diffused image. Then, the local diffusion energy term is constructed by performing homomorphic unsharp masking operation on diffused image so as to implement a local piecewise constant search. To avoid trapping into local minimum produced by local energy, the global diffusion energy term is formed by approximating diffused image in a global piecewise constant way. Besides, the regularization energy term is included to have penalization effect on evolving contour length and maintenance of level set function being signed distance function. By minimizing the overall energy functional which is a linear combination of local energy, global energy and regularization energy, the evolving contour can be driven to approach the object boundary. The experiments on different characteristics of complex images have shown that the proposed method can achieve satisfying segmentation performance accompanied with some good properties, i.e. the robustness to initial parameter and contour setting, noise insensitivity, quick and stable convergence.