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首页> 外文会议>The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议）论文集 >A Hybrid Segmentation Approach Based on Fuzzy Connectedness and Voronoi Diagram
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A Hybrid Segmentation Approach Based on Fuzzy Connectedness and Voronoi Diagram

机译：基于模糊连通度和Voronoi图的混合分割方法

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This paper presents a hybrid segmentation approach for medical images that requires minimal manual initialization by integrating the fuzzy connectedness and Voronoi diagram classification algorithms. We start with a fuzzy connectedness filter to generate a sample of tissue from a region to be segmented and obtain image statistics that constitute the homogeneity operator to be used in the next stage. The output of the fuzzy connectedness filter is used as a prior to the Voronoi diagram classification filter. This filter performs iterative subdivision and classification of the segmented image resulting in an estimation of the boundary. The output of this filter is a binary image that can be used to display the 2D or 3D result of the segmentation. Comparing with other medical images segmentation approaches, this hybrid approach integrates boundary-based and region-based segmentation methods that amplify the strength but reduce the weakness of both techniques. The collaboration between two different methodologies tends to result in robustness and higher segmentation quality. We have already realized this approach in our medical images application and got a satisfying result.
机译：本文提出了一种用于医学图像的混合分割方法，该方法通过集成模糊连接性和Voronoi图分类算法，需要最少的手动初始化。我们从模糊连通性滤波器开始，从要分割的区域生成组织样本，并获得构成下一阶段要使用的同质算子的图像统计信息。模糊连通性滤波器的输出用作Voronoi图分类滤波器的优先级。该过滤器对分割后的图像进行迭代细分和分类，从而得出边界估计值。该过滤器的输出是二进制图像，可用于显示分割的2D或3D结果。与其他医学图像分割方法相比，此混合方法集成了基于边界和基于区域的分割方法，这些方法可以增强强度，同时可以减少这两种技术的缺点。两种不同方法之间的协作往往会导致鲁棒性和更高的细分质量。我们已经在医学图像应用中实现了这种方法，并获得了令人满意的结果。

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