Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort |
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| Authors: | Chun Chen Emile A. Hendriks Robert P. W. Duin Johan H. C. Reiber Pieter S. Hiemstra Letty A. de Weger Berend C. Stoel |
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| Affiliation: | (1) Information and Communication Theory Group, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands;(2) Department of Pulmonology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands;(3) Department of Radiology, Division of Image Processing, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands;(4) Present address: Signals & Systems group, University of Twente, Haaksbergerstraat 174-613, 7513EB Enschede, The Netherlands |
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| Abstract: | Quantification of airborne pollen is an important tool in scientific research and patient care in allergy. The currently available method relies on microscopic examination of pollen slides, performed by qualified researchers. Although highly reliable, the method is labor intensive and requires extensive training of the researchers involved. In an approach to develop alternative detection methods, we performed a feasibility study on the automated recognition of the allergenic relevant pollen, grass, birch, and mugwort, by utilizing digital image analysis and pattern recognition tools. Of a total of 254 pollen samples (including 79 of grass, 79 of birch and 96 of mugwort), 97.2% were recognized correctly. This encouraging result provides a promising prospect for future developments. |
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| Keywords: | Airborne pollen Automated recognition Image analysis Pore/colpus Shape features Statistical gray-level features |
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