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The Segmentation of 3D Images Using the Random Walking Technique on a Randomly Created Image Adjacency Graph

机译:在随机创建的图像邻接图上使用随机行走技术对3D图像进行分割

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This paper considers the problem of image segmentation using the random walker algorithm. In the case of 3D images, the method uses an extreme amount of memory and time resources. These are required in order to represent the corresponding enormous image graph and to solve the resulting sparse linear system. Having in mind these limitations, this paper proposes techniques for the optimization of the random walker approach. The optimization is obtained by processing supervoxels representing homogeneous image regions rather than single voxels. A fast and efficient method for supervoxel determination is introduced. A method for the creation of an image adjacency graph from an irregular grid of supervoxels is also proposed. The results of applying the introduced approach to segmentation of 3D CT data sets are presented and compared with the results of the original random walker approach and other state-of-the-art methods. The accuracy and the computational overhead is regarded in the comparison. The analysis of results shows that the modified method can be successfully applied for the segmentation of volumetric images and provides results in a reasonable time without a significant loss in the image segmentation accuracy. It also outperforms the state-of-the-art methods considered in the comparison.
机译:本文考虑了使用随机沃克算法进行图像分割的问题。在3D图像的情况下,该方法使用大量的存储器和时间资源。这些是表示相应的巨大图像图并解决所得的稀疏线性系统所必需的。考虑到这些限制,本文提出了用于优化随机沃克方法的技术。通过处理代表均匀图像区域的超级体素而不是单个体素来获得优化。介绍了一种快速有效的超体素测定方法。还提出了一种从超体素的不规则网格创建图像邻接图的方法。提出了将引入的方法应用于3D CT数据集分割的结果,并将其与原始随机沃克方法和其他最新方法的结果进行了比较。比较中考虑了准确性和计算开销。结果分析表明,改进后的方法可以成功地应用于体积图像的分割,并在合理的时间内提供结果,而不会明显降低图像的分割精度。它也优于比较中考虑的最新方法。

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