title: Detection of pollen grains in multifocal optical microscopy images of air samples.
abstract: Pollen is a major cause of allergy and monitoring pollen in the air is relevant for diagnostic purposes, development of pollen forecasts, and for biomedical and biological researches. Since counting airborne pollen is a time-consuming task and requires specialized personnel, an automated pollen counting system is desirable. In this article, we present a method for detecting pollen in multifocal optical microscopy images of air samples collected by a Burkard pollen sampler, as a first step in an automated pollen counting procedure. Both color and shape information was used to discriminate pollen grains from other airborne material in the images, such as fungal spores and dirt. A training set of 44 images from successive focal planes (stacks) was used to train the system in recognizing pollen color and for optimization. The performance of the system has been evaluated using a separate set of 17 image stacks containing 65 pollen grains, of which 86% was detected. The obtained precision of 61% can still be increased in the next step of classifying the different pollen in such a counting system. These results show that the detection of pollen is feasible in images from a pollen sampler collecting ambient air. This first step in automated pollen detection may form a reliable basis for an automated pollen counting system.
authors.affiliation: Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands.
authors.forename: Sander H
authors.initials: SH
authors.lastname: Landsmeer
authors.forename: Emile A
authors.initials: EA
authors.lastname: Hendriks
authors.forename: Letty A
authors.initials: LA
authors.lastname: de Weger
authors.forename: Johan H C
authors.initials: JH
authors.lastname: Reiber
authors.forename: Berend C
authors.initials: BC
authors.lastname: Stoel
mesh_terms: D000388:Air / Q000032:analysis*; D001331:Automation / Q000379:methods; D003116:Color; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008853:Microscopy / Q000379:methods*; D011058:Pollen / Q000648:ultrastructure*; D012680:Sensitivity and Specificity