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.
Similar content being viewed by others
References
Alvarez-Borrego J, Mourino R, Cristobal G, Pech-Pacheco J (2000) Invariant optical color correlation for recognition of Vibrio cholera. 01. IEEE Int Conf Pattern Recognition 2847:283–286
Chavez de Paz LE (2009) Image analysis software based on color segmentation for characterization of viability and physiological activity of biofilms. Appl Environ Microbiol 75:1734–1739
Chi F, Shen SH, Cheng HP, JingYX YYG, Dazzo FB (2005) Ascending migration of endophytic rhizobia from roots to leaves inside rice plants and assessment of their benefits to the growth physiology of rice. Appl Environ Microbiol 71:7271–7278
Daims H, Wagner M (2007) Quantification of uncultured microorganisms by fluorescence microscopy and digital image analysis. Appl Microbiol Biotechnol 75:237–248
Daims H, Nielsen J, Nielsen P, Schileifer KH, Wagner M (2001) In situ characterization of Nitrospira-like nitrite-oxidizing bacteria active in wastewater treatment plants. Appl Environ Microbiol 67:5273–5284
Dazzo FB (2004) Applications of quantitative microscopy in studies of plant surface microbiology. In: Varma A, Abbott L, Werner D, Hampp R (eds) Plant surface microbiology. Springer, Germany, pp 503–550
Dazzo FB, Schmid M, Hartmann A (2007) Immunofluorescence microscopy and fluorescence in situ hybridization combined with CMEIAS and other image analysis tools for soil- and plant-associated microbial autecology. In: Garland J, Hurst C, Lipson D, Mills A, Stetzenbach L, Crawford R (eds) Manual of environmental microbiology, 3rd edn. American Society for Microbiology Press, Washington, pp 712–733
Dethlefsen L, Relman DA (2007) The importance of individuals and scale: moving towards single-cell microbiology. Environ Microbiol 9:8–10
Dubuisson MP, Jain AK, Jain MK (1994) Segmentation and classification of bacterial culture images. J Microb Meth 19:279–295
Fernandez A, Hashsham S, Dollhopf D, Raskin L, Glagoleva O, Dazzo FB, Hickey R, Criddle C, Tiedje JM (2000) Flexible community structure correlates with stable community function in methanogenic bioreactor communities perturbed by glucose. Appl Environ Microbiol 66:4058–4067
Forero MG, Sroubek F, Cristobal G (2004) Identification of tuberculosis bacteria based on shape and color. Real Time Imaging 10:251–262
Fukuda M, Matsuyama J, Katano T, Nakano S, Dazzo FB (2006) Assessing primary and bacterial production rates in biofilms on pebbles in Ishite Stream, Japan. Microb Ecol 52:1–7
Fuh CS, Cho SW, Essig K (2000) Hierarchical color image region segmentation for content-based image retrieval system. IEEE Trans Image Process 9:156–162
Gantner S, Schmid M, Dürr C, Schuhegger R, Steidle A, Hutzler P, Langebartels C, Eberl L, Hartmann A, Dazzo FB (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
Grimm V, Railsback SF (2005) Individual-based modelling and ecology. Princeton University Press, Princeton, p 428
Hashsham S, Fernandez A, Dollhopf S, Dazzo FB, Hickey R, Tiedje JM et al (2000) Parallel processing of substrate correlates with greater functional stability in methanogenic bioreactor communities perturbed by glucose. Appl Environ Microbiol 66:4050–4057
Lee N, Nielsen PH, Andreasen K, Juretschko S, Nielsen J, Schleifer K et al (1999) Combination of fluorescent in situ hybridization and microautoradiography—a new tool for structure–function analyses in microbial ecology. Appl Environ Microbiol 65:1289–1297
Leveau JJ, Loper JE, Lindow SE (2007) Reporter gene systems useful in evaluating in situ gene expression by soil- and plant-associated bacteria. In: Garland J, Hurst C, Lipson D, Mills A, Stetzenbach L, Crawford R (eds) Manual of environmental microbiology, 3rd edn. American Society for Microbiology Press, Washington, pp 734–747
Liu J, Dazzo FB, Glagoleva O, Yu B, Jain A (2001) CMEIAS: a computer-aided system for the image analysis of bacterial morphotypes in microbial communities. Microb Ecol 41:173–194
Nielsen J, Christensen D, Kloppenborg M, Nielsen P (2003) Quantification of cell-specific substrate uptake by probe-defined bacteria under in situ conditions by microautoradiography and fluorescence in situ hybridization. Environ Microbiol 5:202–211
Prosser JI, Bohannan BJ, Curtis TP, Ellis RJ, Firestone MK, Freckleton RP, Green JL, Green LE, Killham K, Lennon JJ, Osborn AM, Solan M, van der Gast CJ, Young JP (2007) The role of ecological theory in microbial ecology. Nature Rev Microbiol 5:384–392
Reddy CK, Dazzo FB (2004) Computer-assisted segmentation of bacteria in color micrographs. Microscopy Anal 18:5–7
Reddy CK, Liu FI, Dazzo FB (2003) Semi-automated segmentation of microbes in color images. In: Eschbach R, Marcu G (eds) Color imaging VIII: processing, hardcopy, and applications. Proc SPIE–International Society for Optical Engineering and The Society for Imaging Science and Technology, vol 5008, Bellingham, Washington, pp 548–559
Shopov A, Williams SC, Verity PG (2000) Improvements in image analysis and fluorescence microscopy to discriminate and enumerate bacteria and viruses in aquatic samples. Aquatic Microb Ecol 22:103–110
Sieracki ME, Reichenbach SE, Webb KL (1989) Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis. Appl Environ Microbiol 55:2762–2772
Wilkinson MH, Schut F (1998) Digital image analysis of microbes: imaging, morphometry, fluorometry, and motility techniques and applications. Wiley, UK
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco, p 525
Wyszecki G, Styles WS (1982) Color science: concepts and methods, quantitative data and formulae, 2nd edn. Wiley, NY
Zhou J, Fang X, Ghosh BK (1996) Multiresolution filtering with application to image segmentation. Math Comp Modeling 24:177–195
Zhou Z, Pons M, Raskin L, Ziles J (2007) Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples. Appl Environ Microbiol 73:2956–2962
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gross, C.A., Reddy, C.K. & Dazzo, F.B. CMEIAS Color Segmentation: An Improved Computing Technology to Process Color Images for Quantitative Microbial Ecology Studies at Single-Cell Resolution. Microb Ecol 59, 400–414 (2010). https://doi.org/10.1007/s00248-009-9616-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00248-009-9616-7