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首页> 外文期刊>Medical image analysis >Tensor classification of N-point correlation function features for histology tissue segmentation.
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Tensor classification of N-point correlation function features for histology tissue segmentation.

机译:用于组织学组织分割的N点相关函数特征的张量分类。

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摘要

In this paper, we utilize the N-point correlation functions (N-pcfs) to construct an appropriate feature space for achieving tissue segmentation in histology-stained microscopic images. The N-pcfs estimate microstructural constituent packing densities and their spatial distribution in a tissue sample. We represent the multi-phase properties estimated by the N-pcfs in a tensor structure. Using a variant of higher-order singular value decomposition (HOSVD) algorithm, we realize a robust classifier that provides a multi-linear description of the tensor feature space. Validated results of the segmentation are presented in a case-study that focuses on understanding the genetic phenotyping differences in mouse placentae.
机译:在本文中,我们利用N点相关函数(N-pcfs)来构建适当的特征空间,以在组织学染色的显微图像中实现组织分割。 N-pcfs估计组织样本中的微结构成分堆积密度及其空间分布。我们在张量结构中表示由N-pcfs估计的多相属性。使用高阶奇异值分解(HOSVD)算法的变体,我们实现了一种鲁棒的分类器,该分类器提供了张量特征空间的多线性描述。在个案研究中介绍了分割的验证结果,该研究重点在于了解小鼠胎盘的遗传表型差异。

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