Mosaliganti Kishore, Janoos Firdaus, Irfanoglu Okan, Ridgway Randall, Machiraju Raghu, Huang Kun, Saltz Joel, Leone Gustavo, Ostrowski Michael
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA.
Med Image Anal. 2009 Feb;13(1):156-66. doi: 10.1016/j.media.2008.06.020. Epub 2008 Jul 25.
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.
在本文中，我们利用N点相关函数（N-pcfs）构建一个合适的特征空间，以实现组织学染色显微图像中的组织分割。N-pcfs估计组织样本中微观结构成分的堆积密度及其空间分布。我们将由N-pcfs估计的多相属性表示为张量结构。使用高阶奇异值分解（HOSVD）算法的一个变体，我们实现了一个强大的分类器，该分类器提供了张量特征空间的多线性描述。在一个专注于理解小鼠胎盘基因表型差异的案例研究中展示了分割的验证结果。
