Substructural segmentation based on regional shape differences

A. Machado, J. Gee, M. Campos
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引用次数: 2

Abstract

This article presents a method for the segmentation of substructures based on exploratory factor analysis. In this approach, a set of high-dimensional shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to morphometric investigations.
基于区域形状差异的子结构分割
提出了一种基于探索性因子分析的子结构分割方法。在这种方法中,检查一组高维形状相关变量,目的是寻找具有强相关性的聚类。这种聚类可以潜在地识别具有解剖意义的区域,从而为形态计量学研究提供见解。
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