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A novel technique to extract accurate cell contours applied to analysis of phytoplankton images
Department of Electrical Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory. Department of Electrical Power Systems, Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Department of Electrical Power Systems & Department of Information Systems, Kaunas University of Technology, Kaunas, Lithuania.
Department of Electrical Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
2015 (English)In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 26, no 2-3, 305-315 p.Article in journal (Refereed) Published
Abstract [en]

Active contour model (ACM) is an image segmentation technique widely applied for object detection. Most of the research in ACM area is dedicated to the development of various energy functions based on physical intuition. Here, instead of constructing a new energy function, we manipulate values of ACM parameters to generate a multitude of potential contours, score them using a machine-learned ranking technique, and select the best contour for each object in question. Several learning-to-rank (L2R) methods are evaluated with a goal to choose the most accurate in assessing the quality of generated contours. Superiority of the proposed segmentation approach over the original boosted edge-based ACM and three ACM implementations using the level-set framework is demonstrated for the task of Prorocentrum minimum cells’ detection in phytoplankton images. Experiments show that diverse set of contour features with grading learned by a variant of multiple additive regression trees (λ-MART) helped to extract precise contour for 87.6 % of cells tested.

Place, publisher, year, edition, pages
Heidelberg: Springer Berlin/Heidelberg, 2015. Vol. 26, no 2-3, 305-315 p.
Keyword [en]
Active contour model (ACM), Object detection, Machine-learned ranking, Phytoplankton
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:hh:diva-27450DOI: 10.1007/s00138-014-0643-0ISI: 000364807600002PubMedID: 26573936Scopus ID: 2-s2.0-84960360101OAI: oai:DiVA.org:hh-27450DiVA: diva2:778099
Note

Microscopy images of Prorocentrum minimum cells were obtained by dr. Ricardas Paskauskas and dr. Sigitas Sulcius at Coastal Research and Planning Institute, Klaipeda University. Funding for this work was provided by a Grant (No. LEK-09/2012) from the Research Council of Lithuania under National Research Programme “Ecosystems in Lithuania: climate change and human impact”. 

Available from: 2015-01-09 Created: 2015-01-09 Last updated: 2017-05-18Bibliographically approved

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