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首页> 外文会议>2011 IEEE Conference on Computer Vision and Pattern Recognition >A Multichannel Edge-Weighted Centroidal Voronoi Tessellation algorithm for 3D super-alloy image segmentation
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A Multichannel Edge-Weighted Centroidal Voronoi Tessellation algorithm for 3D super-alloy image segmentation

机译：用于3D超级合金图像分割的多通道边缘加权质心Voronoi镶嵌算法

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摘要

In material science and engineering, the grain structure inside a super-alloy sample determines its mechanical and physical properties. In this paper, we develop a new Multichannel Edge-Weighted Centroidal Voronoi Tessellation (MCEWCVT) algorithm to automatically segment all the 3D grains from microscopic images of a super-alloy sample. Built upon the classical k-means/CVT algorithm, the proposed algorithm considers both the voxel-intensity similarity within each cluster and the compactness of each cluster. In addition, the same slice of a super-alloy sample can produce multiple images with different grain appearances using different settings of the microscope. We call this multichannel imaging and in this paper, we further adapt the proposed segmentation algorithm to handle such multichannel images to achieve higher grain-segmentation accuracy. We test the proposed MCEWCVT algorithm on a 4-channel Ni-based 3D super-alloy image consisting of 170 slices. The segmentation performance is evaluated against the manually annotated ground-truth segmentation and quantitatively compared with other six image segmentation/edge-detection methods. The experimental results demonstrate the higher accuracy of the proposed algorithm than the comparison methods.
机译：在材料科学和工程中，超级合金样品内部的晶粒结构决定了其机械和物理性能。在本文中，我们开发了一种新的多通道边缘加权质心Voronoi镶嵌（MCEWCVT）算法，以自动分割超级合金样品显微图像中的所有3D晶粒。该算法基于经典的k均值/ CVT算法，既考虑了每个聚类中的体素强度相似性，又考虑了每个聚类的紧凑性。另外，使用显微镜的不同设置，同一片超级合金样品可以产生具有不同晶粒外观的多个图像。我们将其称为多通道成像，在本文中，我们将进一步对提出的分割算法进行调整，以处理此类多通道图像，以实现更高的颗粒分割精度。我们在由170个切片组成的基于Ni的4通道3D超级合金图像上测试了提出的MCEWCVT算法。针对手动注释的地面真相分割评估了分割性能，并与其他六种图像分割/边缘检测方法进行了定量比较。实验结果表明，与比较方法相比，该算法具有更高的精度。

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