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Incorporating Feature Based Priors into the Geodesic Active Contour Model and its Application in Biomedical Imagery

机译:将特征基准器结合到Geodesic Active Contour模型中及其在生物医学图像中的应用

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This paper presents improvements to the geodesic active contour (GAC) model obtained by incorporating user defined prior information into the model itself. Specifically, the stopping function in the GAC model is revised by designing an indicator function derived from a-priori information. The numerical implementation is based on the level set technique. Experimental results illustrate that our approach is efficient and feasible for both artificial and real images. In particular, the proposed method performs well in situations where existing methods are known to fail.
机译:本文介绍了通过将用户定义的先前信息结合到模型本身而获得的测地有源轮廓(GAC)模型的改进。具体地,通过设计从a-priori信息导出的指示器函数来修订GAC模型中的停止功能。数值实现基于级别集技术。实验结果表明,我们的方法对于人造和真实的图像来说是有效和可行的。特别地,所提出的方法在已知现有方法失败的情况下表现良好。

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