Machine vision application to the detection of water-borne micro-organisms

Issue title: Intelligent Knowledge Processing and Decision Making Techniques

Guest editors: Chee Peng Lim, Jadranka Sunde and Lakhmi C. Jain

Article type: Research Article

Authors: Fernandez-Canque, Hernando^{a; *} | Hintea, Sorin^b | Csipkes, Gabor^b | Bota, Sorin^a | Smith, Huw^c

Affiliations: [a] Glasgow Caledonian University, School of Engineering & Computing, Cowcaddens Road, Glasgow. G4 0BA, United Kingdom | [b] Faculty of Electronics and Telecommunication, Technical University of Cluj Napoca, Str. Baritiu, Nr. 26-28, 3400 Cluj Napoca, Romania | [c] Stobhill General Hospital, Scottish Parasite Diagnostic Laboratory, Springburn, Glasgow. G21 3UW, United Kingdom

Correspondence: [*] Corresponding author. E-mail: [email protected]

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. The machine vision proposed provides a 100% detection of cryptosporidium micro-organism as test case. 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.

Keywords: Machine vision, image processing, water quality

DOI: 10.3233/IDT-2009-0050

Journal: Intelligent Decision Technologies, vol. 3, no. 2, pp. 93-100, 2009