Automated Analysis and Classification of Infected Macrophages Using Bright-Field Amplitude Contrast Data

This article presents a methodology for acquisition and analysis of bright-field amplitude contrast image data in high-throughput screening (HTS) for the measurement of cell density, cell viability, and classification of individual cells into phenotypic classes. We present a robust image analysis pi...

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Bibliographic Details
Published inJournal of biomolecular screening Vol. 17; no. 3; pp. 401 - 408
Main Authors Adiga, Umesh, Taylor, Debbie, Bell, Brian, Ponomareva, Larissa, Kanzlemar, Stephen, Kramer, Ryan, Saldanha, Roland, Nelson, Sandra, Lamkin, Thomas J.
Format Journal Article
LanguageEnglish
Published United States 01.03.2012
Subjects
Algorithms
Cell Count
Cell Survival
Francisella tularensis
Humans
Image Enhancement - methods
Image Processing, Computer-Assisted - methods
Macrophages - microbiology
Microscopy - methods
Pattern Recognition, Automated - methods
Phenotype
Tularemia - diagnosis
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ISSN2472-5552
1552-454X
DOI10.1177/1087057111426902

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Summary:This article presents a methodology for acquisition and analysis of bright-field amplitude contrast image data in high-throughput screening (HTS) for the measurement of cell density, cell viability, and classification of individual cells into phenotypic classes. We present a robust image analysis pipeline, where the original data are subjected to image standardization, image enhancement, and segmentation by region growing. This work develops new imaging and analysis techniques for cell analysis in HTS and successfully addresses a particular need for direct measurement of cell density and other features without using dyes.
ISSN:2472-5552
1552-454X
DOI:10.1177/1087057111426902