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

Cite this

@article{ae1cfc7a6d02474ca642d8cff7594d8a,

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.",

keywords = "water quality, image processing, machine vision",

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

note = "<p>Originally published in: Intelligent Decision Technologies (2009), 3 (2), pp.93-100.</p>",

year = "2009",

month = jan,

day = "1",

doi = "10.3233/IDT-2009-0050",

language = "English",

volume = "3",

pages = "93--100",

journal = "Intelligent Decision Technologies",

issn = "1872-4981",

publisher = "IOS Press",

number = "2",

}

TY - JOUR

T1 - Machine vision application to the detection of water-borne micro-organisms

AU - Fernandez-Canque, Hernando

AU - Hintea, Sorin

AU - Csipkes, Gabor

AU - Bota, Sorin

AU - Smith, Huw

N1 - <p>Originally published in: Intelligent Decision Technologies (2009), 3 (2), pp.93-100.</p>

PY - 2009/1/1

Y1 - 2009/1/1

N2 - 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.

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.

KW - water quality

KW - image processing

KW - machine vision

U2 - 10.3233/IDT-2009-0050

DO - 10.3233/IDT-2009-0050

M3 - Article

VL - 3

SP - 93

EP - 100

JO - Intelligent Decision Technologies

JF - Intelligent Decision Technologies

SN - 1872-4981

IS - 2

ER -

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

Abstract

Keywords

Documents and Links

Fingerprint

Cite this