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Microbial Ecology

, Volume 59, Issue 2, pp 400–414 | Cite as

CMEIAS Color Segmentation: An Improved Computing Technology to Process Color Images for Quantitative Microbial Ecology Studies at Single-Cell Resolution

  • Colin A. Gross
  • Chandan K. Reddy
  • Frank B. Dazzo
Methods

Abstract

Quantitative microscopy and digital image analysis are underutilized in microbial ecology largely because of the laborious task to segment foreground object pixels from background, especially in complex color micrographs of environmental samples. In this paper, we describe an improved computing technology developed to alleviate this limitation. The system’s uniqueness is its ability to edit digital images accurately when presented with the difficult yet commonplace challenge of removing background pixels whose three-dimensional color space overlaps the range that defines foreground objects. Image segmentation is accomplished by utilizing algorithms that address color and spatial relationships of user-selected foreground object pixels. Performance of the color segmentation algorithm evaluated on 26 complex micrographs at single pixel resolution had an overall pixel classification accuracy of 99+%. Several applications illustrate how this improved computing technology can successfully resolve numerous challenges of complex color segmentation in order to produce images from which quantitative information can be accurately extracted, thereby gain new perspectives on the in situ ecology of microorganisms. Examples include improvements in the quantitative analysis of (1) microbial abundance and phylotype diversity of single cells classified by their discriminating color within heterogeneous communities, (2) cell viability, (3) spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within rhizoplane biofilms, and (4) biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization. The stand-alone executable file plus user manual and tutorial images for this color segmentation computing application are freely available at http://cme.msu.edu/cmeias/. This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most, thereby strengthening quantitative microscopy-based approaches to advance microbial ecology in situ at individual single-cell resolution.

Keywords

Foreground Object Foreground Pixel Color Segmentation Ground Truth Image Training Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by Research Excellence Funds from the Centers for Microbial Ecology, Renewable Organic Resources, and Microbial Pathogenesis at Michigan State University, and the Kellogg Biological Station Long-Term Ecological Research program. We thank colleagues who provided images listed in Table 1, Jim Tiedje, Tom Schmidt, George Stockman, and Rawle Hollingsworth for advice and support, and Stephan Gantner and George Kowalchuk for reviewing the manuscript.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Colin A. Gross
    • 1
    • 3
  • Chandan K. Reddy
    • 2
    • 4
  • Frank B. Dazzo
    • 1
    • 2
  1. 1.Department of Microbiology & Molecular GeneticsMichigan State UniversityEast LansingUSA
  2. 2.Center for Microbial EcologyMichigan State UniversityEast LansingUSA
  3. 3.Surgery Branch, National Cancer InstituteNational Institutes of HealthBethesdaUSA
  4. 4.Department of Computer ScienceWayne State UniversityDetroitUSA

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