An Integrated Approach to Analysis of Phytoplankton Images
2015 (English)In: IEEE Journal of Oceanic Engineering, ISSN 0364-9059, Vol. 40, no 2, 315-326 p.Article in journal (Refereed) Published
The main objective of this paper is detection, recognition, and abundance estimation of objects representing the Prorocentrum minimum (Pavillard) Schiller (P. minimum) species in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. The proposed technique for solving the task exploits images of two types, namely, obtained using light and fluorescence microscopy. Various image preprocessing techniques are applied to extract a variety of features characterizing P. minimum cells and cell contours. Relevant feature subsets are then selected and used in support vector machine (SVM) as well as random forest (RF) classifiers to distinguish between P. minimum cells and other objects. To improve the cell abundance estimation accuracy, classification results are corrected based on probabilities of interclass misclassification. The developed algorithms were tested using 158 phytoplankton images. There were 920 P. minimum cells in the images in total. The algorithms detected 98.1% of P. minimum cells present in the images and correctly classified 98.09% of all detected objects. The classification accuracy of detected P. minimum cells was equal to 98.9%, yielding a 97.0% overall recognition rate of P. minimum cells. The feature set used in this work has shown considerable tolerance to out-of-focus distortions. Tests of the system by phytoplankton experts in the cell abundance estimation task of P. minimum species have shown that its performance is comparable or even better than performance of phytoplankton experts exhibited in manual counting of artificial microparticles, similar to P. minimum cells. The automated system detected and correctly recognized 308 (91.1%) of 338 P. minimum cells found by experts in 65 phytoplankton images taken from new phytoplankton samples and erroneously assigned to the P. minimum class 3% of other objects. Note that, due to large variations of texture and size of P. minimum cells as well as- background, the task performed by the system was more complex than that performed by the experts when counting artificial microparticles similar to P. minimum cells.
Place, publisher, year, edition, pages
New York, NY: IEEE Oceanic Engineering Society, 2015. Vol. 40, no 2, 315-326 p.
Classification committee, feature extraction, feature selection, phytoplankton images, Prorocentrum minimum, random forests (RFs), support vector machine (SVM)
IdentifiersURN: urn:nbn:se:hh:diva-26558DOI: 10.1109/JOE.2014.2317955ScopusID: 2-s2.0-84900895428OAI: oai:DiVA.org:hh-26558DiVA: diva2:748728
This work was supported by the Research Council of Lithuania under Grant LEK-09/2012.2014-09-222014-09-222015-04-13Bibliographically approved