A Comparison of Metrics and Algorithms for Fiber Clustering

Viviana Siless, Sergio Medina, G. Varoquaux, B. Thirion
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引用次数: 17

Abstract

Diffusion-weighted Magnetic Resonance Imaging (dMRI) can unveil the microstructure of the brain white matter. The analysis of the anisotropy observed in the dMRI contrast with tractography methods can help to understand the pattern of connections between brain regions and characterize neurological diseases. Because of the amount of information produced by such analyses and the errors carried by the reconstruction step, it is necessary to simplify this output. Clustering algorithms can be used to group samples that are similar according to a given metric. We propose to explore the well-known clustering algorithm k-means and a recently available one, Quick Bundles [1]. We propose an efficient procedure to associate k-means with Point Density Model, a recently proposed metric to analyze geometric structures. We analyze the performance and usability of these algorithms on manually labeled data and a database a 10 subjects.
光纤聚类的度量和算法比较
弥散加权磁共振成像(dMRI)可以揭示脑白质的微观结构。dMRI观察到的各向异性与神经束造影方法的对比分析可以帮助理解脑区域之间的连接模式和表征神经系统疾病。由于这种分析产生的信息量和重建步骤所带来的误差,有必要简化这种输出。聚类算法可用于根据给定的度量对相似的样本进行分组。我们建议探索著名的聚类算法k-means和最近可用的Quick Bundles[1]。我们提出了一种将k-means与最近提出的用于分析几何结构的度量点密度模型(Point Density Model)相关联的有效方法。我们分析了这些算法在人工标记数据和包含10个主题的数据库上的性能和可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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