This website provides information on automated colony counting using a non-commercial open-source system.
Manual counting of bacterial colony forming units (CFUs) on agar plates is laborious and error-prone. We therefore implemented a colony counting system with a novel segmentation algorithm to discriminate bacterial colonies from blood and other agar plates.
A colony counter hardware was designed and a novel segmentation algorithm was written in MATLAB®. In brief, pre-processing with Top-Hat-filtering to obtain a uniform background was followed by the segmentation step, during which the colony images were extracted from the blood agar and individual colonies were separated. A Bayes classifier was then applied to count the final number of bacterial colonies as some of the colonies could still be concatenated to form larger groups.
To assess accuracy and performance of the colony counter, we tested automated colony counting of different agar plates with known CFU numbers of S. pneumoniae, P. aeruginosa and M. catarrhalis and showed excellent performance.
Article in PLoS ONE:
Brugger SD, Baumberger C, Jost M, Jenni W, Brugger U, et al. (2012) Automated Counting of Bacterial Colony Forming Units on Agar Plates. PLoS ONE 7(3): e33695. doi:10.1371/journal.pone.0033695
Free Full Text Article
The ColonyCounter team:
Christian Baumberger, Silvio Brugger, Urs Brugger, Pascal Frey, Horst Heck, Markus Hilty, Werner Jenni, Marcel Jost & Kathrin Mühlemann†
This work has been supported by the Institute for Infectious Diseases, University of Bern (www.ifik.unibe.ch) and the Bern University of Applied Sciences (www.ti.bfh.ch), Bern, Switzerland.
Can I analyze pictures taken with my own digital cam?
Unfortunately, the current version only supports the published system with blue ring illumination and pictures taken with an IDS uEye® camera. However, we are happy to announce that an updated version of the software is being developed which should allow the use of pictures generated with other systems. In principle, it is possible to use the test mode of the stand-alone application (cc_test.bat) after installation of the MCR from MATLAB® (it might work without if MATLAB® is installed) and count pictures taken with another camera (you can try that with your pics). Pics should be TIFF files, RGB24 (8bit), 1441x1441. Please note that accuracy might be insufficient as the algorithm relies on blue ring illuminated pictures.
I tried to open cc.exe on my computer but the software is not working. What should I do?
Please be sure that you are running the software with the components mentioned in the paper on a PC with Windows XP SPII® and the correct version of the uEye® Driver package (see article, provided on this website). We cannot offer a professional troubleshooting service. However, we always try to help as much as we can and you also can run the test mode (cc_test.bat) with test images (see download section) to test the functionality of the system and an updated version which allows the use of other picture sources should be available in the future.
I am a programmer, can I adapt your program to my needs to improve counting performance?
Yes! You are very welcome to do that. This is open-source software and can be adapted provided the original author and source are credited.
Do you adapt the system to my needs if requested?
We cannot offer such a service as this is a non-commercial project performed in the free-time of the authors. However, there might be an opportunity to discuss such a project with members of the team. Please use the contact address below to start a discussion.
Article in The Scientist ®, May 1st, 2013, by Amber Dance: The Scientist ®: Down for the Count
Article on biocompare.com ®, May 13th, 2014, by Anna Lewcock: biocompare.com ®: See Spots (But in a Good Way) with Automated Colony Counters
Electrosuisse recognition prize 2008 to Christian Baumberger & Marcel Jost for their BSc in electrical engineering thesis: «Automatische Auszählung von Bakterienkolonien» (automated counting of bacterial colonies): electrosuisse
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