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