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

Hernando Fernandez-Canque, Sorin Hintea, Gabor Csipkes, Sorin Bota, Huw Smith

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)93-100
    Number of pages8
    JournalIntelligent Decision Technologies
    Issue number2
    Publication statusPublished - 1 Jan 2009


    • water quality
    • image processing
    • machine vision


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