Publications:Categorizing cells in phytoplankton images


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Title Categorizing cells in phytoplankton images
Author Adas Gelzinis and Antanas Verikas and Marija Bacauskiene and Irina Olenina and Sergej Olenin
Year 2011
PublicationType Conference Paper
HostPublication Recent Advances in Signal Processing, Computational Geometry and Systems Theory
Conference The 11th WSEAS International Conference on Signal Processing, Computational Geometry and Artificial Vision (ISCGAV'11), Florence, Italy, August 23-25, 2011
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Abstract This article is concerned with detection of invasive species---Prorocentrum minimum (P. minimum)---in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization, image segmentation, and SVM and random forest-based classification of objects was developed to solve the task. The developed algorithms were tested using 114 images of 1280 x 960 pixels. There were 2088  P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classify 94.9% of all objects. The results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species.