Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: TAMMiCol: tool for analysis of the morphology of microbial colonies
Author: Tronnolone, H.
Gardner, J.
Sundstrom, J.
Jiranek, V.
Oliver, S.
Binder, B.
Citation: PLoS Computational Biology, 2018; 14(12):e1006629-1-e1006629-15
Publisher: PLoS - Public Library of Science
Issue Date: 2018
ISSN: 1553-734X
Editor: Schneidman-Duhovny, D.
Statement of
Hayden Tronnolone, Jennifer M. Gardner, Joanna F. Sundstrom, Vladimir Jiranek, Stephen G. Oliver, Benjamin J. Binder
Abstract: Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding.
Keywords: Biofilms
Bacillus subtilis
Saccharomyces cerevisiae
Ammonium Sulfate
Culture Media
Computational Biology
Image Processing, Computer-Assisted
Databases, Factual
Rights: © 2018 Tronnolone et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI: 10.1371/journal.pcbi.1006629
Grant ID:
Published version:
Appears in Collections:Aurora harvest 3
Mathematical Sciences publications

Files in This Item:
File Description SizeFormat 
hdl_117863.pdfPublished Version1.46 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.