Sequential Hierarchical Agglomerative Clustering of White Matter Fiber Pathways.
Sequential Hierarchical Agglomerative Clustering of White Matter Fiber Pathways.
复制标题
白质纤维通路的顺序分层聚集聚类。
DOI：
10.1109/tbme.2015.2391913
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Çetingül,HErtan
中科院分区：
文献类型：
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作者：
Demir,Ali;Çetingül,HErtan
白色物质纤维的路径，通过纤维束成像从扩散MRI数据中提取，成束，是一致的neuroanatomy. MethodsWe铸造这个问题的聚类数据流的问题，并使用一个顺序的框架来处理一个纤维的时间。我们的方法，命名为序贯层次凝聚聚类（HAC），表示与参数模型的聚类，执行HAC的相对较少的纤维数量时，参数需要初始化和/或更新，并分配标签到以下的数据流根据当前models.ResultsThe幻影数据上的实验评估我们的方法对初始化和参数调整的敏感性，并显示出其优于替代技术的优点。真实的数据上的实验表明，它的效率和速度在聚类白色物质纤维路径到解剖学上不同bundles.ConclusionSequential HAC是一种快速的方法，受益于有一个预定义数量的集群，并迅速分配标签传入的数据具有高精度。它可以被认为是一种机制，做聚类，同时接受新计算的纤维，从而减轻计算每对纤维之间的距离在tractogram.SignificanceSequentialHAC的负担是一个实用的工具，可以交互式集群纤维路径，并可以集成到纤维跟踪，这将是非常有用的临床研究人员和神经解剖学家。
ObjectiveWe consider the problem of clustering white matter fiber pathways, extracted from diffusion MRI data via tractography, into bundles that are consistent with the neuroanatomy.MethodsWe cast this problem as clustering streams of data, and use a sequential framework to process one fiber at a time. Our method, named as sequential hierarchical agglomerative clustering (HAC), represents the clusters with parametric models, performs HAC of relatively small number of fibers only when the parameters need to be initialized and/or updated, and assigns the labels to the following streams of data according to the current models.ResultsExperiments on phantom data evaluate the sensitivity of our method to initialization and parameter tuning, and show its advantages over alternative techniques. Experiments on real data demonstrate its efficacy and speed in clustering white matter fiber pathways into anatomically distinct bundles.ConclusionSequential HAC is a fast method that benefits from having a predefined number of clusters, and rapidly assigns labels to incoming data with high accuracy. It can be thought of as a mechanism that does clustering, while simultaneously accepting newly computed fibers; thereby, alleviating the burden of computing the distances between every pair of fibers in a tractogram.SignificanceSequential HAC is a practical tool that can interactively cluster fiber pathways and can be integrated into fiber tracking, which will be very useful for clinical researchers and neuroanatomists.