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Related Experiment Video

Updated: Jul 2, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Tensor classification of N-point correlation function features for histology tissue segmentation.

Kishore Mosaliganti¹, Firdaus Janoos, Okan Irfanoglu

¹Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. mosaligk@cse.ohio-state.edu

Medical Image Analysis

|September 3, 2008

Summary

This summary is machine-generated.

This study introduces N-point correlation functions (N-pcfs) for precise tissue segmentation in microscopy images. This method enhances understanding of genetic differences in mouse placentae by analyzing microstructural packing densities.

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Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

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Last Updated: Jul 2, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

Area of Science:

Computational pathology
Microscopic image analysis
Biophysics

Background:

Accurate tissue segmentation is crucial for analyzing microscopic images in biological research.
Traditional methods may struggle with complex microstructural information in histology-stained samples.

Purpose of the Study:

To develop a novel feature space for robust tissue segmentation using N-point correlation functions (N-pcfs).
To apply this method for analyzing genetic phenotyping differences in mouse placentae.

Main Methods:

Utilized N-point correlation functions (N-pcfs) to estimate microstructural constituent packing densities and spatial distribution.
Represented N-pcf-derived properties in a tensor structure.
Employed a variant of the higher-order singular value decomposition (HOSVD) algorithm for classification.

Main Results:

Constructed an effective feature space for histology-stained microscopic image segmentation.
Developed a robust classifier providing a multi-linear description of the tensor feature space.
Successfully applied the method in a case study on mouse placentae.

Conclusions:

N-pcfs offer a powerful approach for feature space construction in microscopic image segmentation.
The developed HOSVD-based classifier demonstrates robustness and multi-linear descriptive capabilities.
This technique facilitates the analysis of microstructural differences related to genetic variations in biological tissues.