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 |
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Pages (from-to) | 93-100 |
Number of pages | 8 |
Journal | Intelligent Decision Technologies |
Volume | 3 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jan 2009 |
Keywords
- water quality
- image processing
- machine vision