Machine Vision Application to the Detection of Micro-organism in Drinking Water

Authors:
Hernando Fernandez-Canque;Sorin Hintea;Gabor Csipkes;Allan Pellow;Huw Smith
Affiliations:
School of Engineering & Computing, Glasgow Caledonian University, Glasgow, United Kingdom G4 0BA;Faculty of Electronics and Telecommunication, Technical University of Cluj Napoca, Cluj Napoca, Romania 3400;Faculty of Electronics and Telecommunication, Technical University of Cluj Napoca, Cluj Napoca, Romania 3400;School of Engineering & Computing, Glasgow Caledonian University, Glasgow, United Kingdom G4 0BA;Scottish Parasite Diagnostic Laboratory Springburn, Stobhill General Hospital, Glasgow, United Kingdom G21 3UW
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Year:
2008

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Abstract

The work presented in this paper uses a novel Machine Vision application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.