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Updated: May 2, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry.

Saurav Basu¹, Soheil Kolouri, Gustavo K Rohde

¹Center for Bioimage Informatics, Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213.

Proceedings of the National Academy of Sciences of the United States of America

|February 20, 2014

Summary

This summary is machine-generated.

This study introduces a new automated method using optimal mass transport to visualize cell differences from microscopic images. It accurately identifies malignancy markers and assay differences without predefined features.

Keywords:

cell morphometry high content screening optimal transport

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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

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Published on: June 30, 2017

Area of Science:

Cellular biology
Computational pathology
Biophysics

Background:

Advanced microscopy yields complex subcellular data beyond visual interpretation.
Quantifying cells with predefined features aids population discrimination but lacks direct biological insight.
Interpreting image-derived cellular data for biological meaning is challenging.

Purpose of the Study:

To develop an automated method for detecting and visualizing phenotypic cell differences.
To enable direct biological interpretation of complex imaging data.
To identify malignancy-associated chromatin patterns and assay differences.

Main Methods:

Application of optimal mass transport theory for comparing cell distributions.
Automated detection and visualization of phenotypic variations.
Analysis of peripheral chromatin distribution in cell nuclei.

Main Results:

Demonstrated association between peripheral chromatin patterns and malignancy in liver and thyroid cells.
Successfully recovered biologically interpretable differences in translocation imaging assays.
The method is fully automated and does not require predefined numerical features.

Conclusions:

Optimal mass transport provides an interpretable framework for analyzing high-content microscopy data.
The developed method offers automated, feature-free detection of significant cellular phenotypes.
This approach enhances the biological interpretation of imaging data for diagnostics and research.