Dazzo, F. B. 2010. CMEIAS digital microscopy and quantitative image analysis of microorganisms. Vol 2, Pages 1083-1090 in A. Mendez-Vilas and J. Diaz, eds. Microscopy: Science, Technology, Applications and Education. Formatex Research Center Microscopy Book Series #4, Badajoz, Spain.

Citable PDF link: https://lter.kbs.msu.edu/pub/2152

A major challenge in microbiology is to develop computing tools that can extract ecologically important information from digital images of populations and communities at single cell resolution, and analyze their structure in situ without cultivation. Several microbial ecologists, mathematicians, statisticians and computer scientists are addressing this challenge by developing a suite of software applications called CMEIAS (Center for Microbial Ecology Image Analysis System. The first release version of CMEIAS applies pattern recognition algorithms to classify all major plus several rare microbial morphotypes with 97% accuracy. Various CMEIAS upgrades feature image processing tools to segment objects within grayscale and color images before analysis, numerous measurement attributes for object analysis and classification, a multilinear cluster analysis application to optimize the size borders for subclassification of each morphotype into operational morphological units, tools to prepare images for extraction of spatial distribution data for pointpattern, quadrat-based and geostatistical analyses of microbial colonization to surfaces, and add-ins to compile, analyze, tabulate, graph and compute ecological statistics on CMEIAS data. When finalized, the various software applications and their documentations (refereed journal publications, thoroughly illustrated user manuals, help topic search files, audio-visual training tutorials with accompanying test images) are released as free downloads at our CMEIAS website http://cme.msu.edu/cmeias. Examples of ongoing research projects using CMEIAS applications include the autecological biogeography of superior endophytic rhizobial inoculants that promote grain production of rice crops, architectural analysis of aquatic microbial biofilms, and microscopical classification of human vaginal microflora in health and disease. This improved computing technology opens new opportunities of digital imaging applications where size, shape, abundance, luminosity, color, architecture and spatial location are important, thereby strengthening quantitative microscopy-based approaches to advance microbial ecology in situ and spatial scales directly relevant to individual microbes.

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