We are pleased to announce the release of CMEIAS Color Segmentation v. 1.0, a free, improved computing technology designed to alleviate the laborious task of segmenting foreground objects of interest (e.g., microbes) from background in complex digital micrographs, as commonly encountered when preparing digital images of microbial populations and communities in environmental samples for quantitative image analysis. The graphical user interface of this new software application and 4 examples of color segmentation in its About Shield are shown here.
The complexity of image segmentation is very high when the microbes of interest are colored to reveal important information on their ecological, biochemical, physiological, cytological and/or phylogenetic characteristics in situ. The unique feature of this new software is its ability to segment images when presented with the difficult yet commonplace challenge of removing background pixels whose 3-dimensional color space overlaps the range that defines the foreground objects. Image segmentation is accomplished by utilizing algorithms to address both color and spatial relationships of the user-selected foreground object pixels, and then to accurately define the boundary of the desired foreground objects using region-growing and mathematical morphology techniques. Extensive performance testing of this new color segmentation algorithm has been evaluated on many complex micrographs at single pixel resolution, with results indicating an overall pixel classification accuracy of 99+% when compared to the corresponding test set of manually edited ground truth data.
The software application also features several other image processing methods to compliment the color segmentation algorithm in producing the final output images that contain the colored microbes of interest fully segmented against a noise-free background and ready for image analysis. These various utilities broaden the range of complex color images that can be processed accurately for quantitative analysis of cell size, morphology, abundance, luminosity and spatial location, thereby adding to the arsenal of tools freely available to microbial ecologists to study their favorite organism or process in situ. The software can be used to process digital images acquired by various types of light microscopy that use color to discriminate features of significant importance to microbial ecology as shown below.
Our publication in Microbial Ecology  provides a detailed description of the system's logic, its quantitative performance testing on numerous color micrographs, examples of its application to process images for quantitative analysis of microbial abundance and phylotype diversity, cell viability, spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within biofilms, and biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization.
This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most, thereby strengthening quantitative microscopy-based approaches to advance in situ microbial ecology. Most importantly, it will assist studies that use color classifications to reveal important quantitative information on the ecology of microorganisms at single-cell resolution, e.g., understanding bacterial individuality to explore the mechanisms through which ecological systems work, how individual cells interact ecophysiologically with each other and their environment, and tests of the emerging theory of individual-based modeling and ecology which predict that individual cell variation is a major driver of population structure and function [2, 3]. The importance of accurately defining microbial processes in situ at the proper spatial scale in which they occur is recognized more and more as ecological theory is deployed to gain a full understanding of microbial ecology . Thus, among the most significant applications of this improved technology will be its use in computer-assisted microscopy to define the spatial scale at which ecologically important events occur among individual, single cells. Our publications using this software application to measure the in situ spatial scale of bacterial cell-to-cell communication (quorum sensing) and the influence of their spatial patterns while colonizing plant roots are available [5-7].
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 Colin A. Gross, Chandan K. Reddy and Frank B. Dazzo. 2010. CMEIAS color segmentation: an improved computing technology to process color images for quantitative microbial ecology studies at single-cell resolution. Microbial Ecology 59: 400-414. DOI:10.1007/s00248-009-9616-7. [abstract | full text]
 Dethlefsen, L. and D. A. Relman. 2007. The importance of individuals and scale: moving towards single-cell microbiology. Environ Microbiol 9: 8-10.
 Grimm, V. and S. F. Railsback. 2005. Individual-based modelling and ecology, Princeton University Press, Princeton, NJ, 428 pp.
 Prosser, J.I., B. J. Bohannan, T. P. Curtis, R. J. Ellis, M. K. Firestone, R. P. Freckleton, J. L. Green, L. E. Green, K. Killham, J. J. Lennon, A. M. Osborn, M. Solan, C. J. van der Gast, and J. P. Young. 2007. The role of ecological theory in microbial ecology. Nature Rev Microbiol 5: 384-392.
 Gantner, S., M. Schmid, C. Dürr, R. Schuhegger, A. Steidle, P. Hutzler, C. Langebartels, L. Eberl, A. Hartmann, F.B. Dazzo. 2006. In situ spatial scale of calling distances and population density-independent N-Acylhomoserine lactone mediated communication by rhizobacteria colonized on plant roots. FEMS Microbiol Ecol 56: 188-194. [abstract | full text]
 Dazzo, F. 2012. CMEIAS-aided microscopy of the spatial ecology of individual bacterial interactions involving cell-to-cell communication within biofilms. Sensors 12: 7047-7062. DOI:10.3390/s120607047
 Dazzo, Frank B., Kevin J. Klemmer, Ryan Chandler and Youssef G. Yanni. 2013. In situ ecophysiology of microbial biofilm communities analyzed by CMEIAS computer-assisted microscopy at single-cell resolution. Diversity (Special issue on Microbial Ecology and Diversity) 5:426-460. DOI:10.3390/d5030426
Requirements and Download of CMEIAS Color Segmentation
- CMEIAS Color Segmentation runs on a PC with 32 or 64-bit operating system (Windows XPpro or later).
- Windows Media Player ver. 11 or higher to view the step-by-step audio-visual demo (CmeiasColorSegmentation.wmv).
- Click here to view and accept the license agreement to download the CMEIASColorSegmentation1.0Setup.exe (16.3 MB), which installs the CMEIAS Color Segmentation program, user manual, training images, audio-visual tutorial demo and chm help file at C:\Program Files\CMEIAS\Cmeias Color Segmentation.
- The user manual pdf can also be viewed online or downloaded independently.