Phase congruency-based detection of circular objects applied to analysis of phytoplankton images

Authors:
A. Verikas;A. Gelzinis;M. Bacauskiene;I. Olenina;S. Olenin;E. Vaiciukynas
Affiliations:
Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania and Intelligent Systems Laboratory, Halmstad University, Box 823, S-30118 Hal ...;Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania;Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania;Department of Marine Research, Environmental Protection Agency, Taikos str., 26, LT-91144 Klaipeda, Lithuania and Coastal Research and Planning Institute, Klaipeda University, H. Manto 84, LT-9229 ...;Coastal Research and Planning Institute, Klaipeda University, H. Manto 84, LT-92294 Klaipeda, Lithuania;Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania
Venue:
Pattern Recognition
Year:
2012

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Abstract

Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. 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-based object contour determination, and SVM- as well as random forest (RF)-based classification of objects was developed to solve the task. A set of various features including a subset of new features computed from phase congruency preprocessed images was used to characterize extracted objects. The developed algorithms were tested using 114 images of 1280x960 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 classified 94.9% of all detected objects. The feature set used has shown considerable tolerance to out-of-focus distortions. The obtained results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species.