A System for Automatic Counting the Number of Collembola Individuals on Petri Disk Images
This paper describes an image processing system developed for automatic counting the number of collembola individuals on petri disks images. The system uses image segmentation and mathematical morphology techniques to identify and count the number of collembolans. The main challenges are the specular reflections at the edges of the circular samples and the foam present in a number of samples. The specular reflections are efficiently identified and removed by performing a two-stage segmentation. The foam is considered to be noise, as it is at cases difficult to discriminate between the foam and the collembola individuals. Morphological image processing tools are used both for noise reduction and for the identification of the collembolans. A total of 38 samples (divided in 3 groups according to their noise level) were tested and the results produced from the automatic system compared to the values available from manual counting. The relative error was on average 5.0% (3.4% for good quality samples, 4.6% for medium quality and 7.5% for poor quality samples).
KeywordsBinary Image Average Relative Error Sample Disk Automatic Counting Greyscale Image
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