View source for Publications:Automated image analysis- and soft computing-based detection of the invasive dinoflagellate Prorocentrum minimum (Pavillard) Schiller

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{{PublicationSetupTemplate|Author=Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Irina Olenina, Sergej Olenin, Evaldas Vaiciukynas
|PID=461861
|Name=Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]);Gelzinis, Adas (Kaunas University of Technology, Lithuania );Bacauskiene, Marija (Kaunas University of Technology, Lithuania );Olenina, Irina (Klaipeda University, Lithuania);Olenin, Sergej (Klaipeda University, Lithuania);Vaiciukynas, Evaldas (Kaunas University of Technology, Lithuania )
|Title=Automated image analysis- and soft computing-based detection of the invasive dinoflagellate Prorocentrum minimum (Pavillard) Schiller
|PublicationType=Journal Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=Expert systems with applications
|JournalISSN=0957-4174
|Status=published
|Volume=39
|Issue=5
|HostPublication=
|Conference=
|StartPage=6069
|EndPage=6077
|Year=2012
|Edition=
|Pages=
|City=Amsterdam
|Publisher=Elsevier
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|DOI=http://dx.doi.org/10.1016/j.eswa.2011.12.006
|ISI=000301155300146
|PMID=
|ScopusId=2-s2.0-84855901953
|NBN=urn:nbn:se:hh:diva-16646
|LocalId=
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|Keywords=Image preprocessing;Phase congruency;Detection of circular objects;Stochastic optimization;Phytoplankton
|Categories=Teknik och teknologier (2)
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|Abstract=<p>A long term goal of this work is an automated system for image analysis- and soft computing-based detection, recognition, and derivation of quantitative concentration estimates of different phytoplankton species using a simple imaging system. This article is limited, however, to detection of objects in phytoplankton images, especially objects representing one invasive species---Prorocentrum minimum (P. minimum),---which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining  phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280 x 960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93.25% of the objects. Bearing in mind simplicity of the imaging system used the result is rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species.</p>
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|CreatedDate=2011-12-05
|PublicationDate=2011-12-05
|LastUpdated=2012-10-20
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:461861}}
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