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

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

doi:10.3233/IDT-2009-0050

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 journal › Article › peer-review

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.

Original language	English
Pages (from-to)	93-100
Number of pages	8
Journal	Intelligent Decision Technologies
Volume	3
Issue number	2
DOIs	https://doi.org/10.3233/IDT-2009-0050
Publication status	Published - 1 Jan 2009

Keywords

water quality
image processing
machine vision

Documents and Links

10.3233/IDT-2009-0050

http://researchonline.gcu.ac.uk/sec/110

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Cite this

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title = "Machine vision application to the detection of water-borne micro-organisms",

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.",

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author = "Hernando Fernandez-Canque and Sorin Hintea and Gabor Csipkes and Sorin Bota and Huw Smith",

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AU - Fernandez-Canque, Hernando

AU - Hintea, Sorin

AU - Csipkes, Gabor

AU - Bota, Sorin

AU - Smith, Huw

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AB - 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.

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