Tensor classification of N-point correlation function features for histology tissue segmentation.
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.
Mosaliganti
Kishore
K
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. mosaligk@cse.ohio-state.edu
Janoos
Firdaus
F
Irfanoglu
Okan
O
Ridgway
Randall
R
Machiraju
Raghu
R
Huang
Kun
K
Saltz
Joel
J
Leone
Gustavo
G
Ostrowski
Michael
M