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A novel technique to extract accurate cell contours applied for segmentation of phytoplankton images

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Abstract

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 (\(\uplambda \)-MART) helped to extract precise contour for 87.6 % of cells tested.

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Authors and Affiliations

  1. Department of Electrical Power Systems, Kaunas University of Technology, Studentu 50, 51368, Kaunas, Lithuania

    Adas Gelzinis, Antanas Verikas, Evaldas Vaiciukynas & Marija Bacauskiene

  2. Centre for Applied Intelligent Systems Research (CAISR), Halmstad University, Kristian IV:s väg 3, PO Box 823, 301 18, Halmstad, Sweden

    Antanas Verikas

  3. Department of Information Systems, Kaunas University of Technology, Studentu 50, 51368, Kaunas, Lithuania

    Evaldas Vaiciukynas

Authors
  1. Adas Gelzinis
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  2. Antanas Verikas
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  3. Evaldas Vaiciukynas
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  4. Marija Bacauskiene
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Corresponding author

Correspondence to Antanas Verikas.

Additional information

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”.

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Gelzinis, A., Verikas, A., Vaiciukynas, E. et al. A novel technique to extract accurate cell contours applied for segmentation of phytoplankton images. Machine Vision and Applications 26, 305–315 (2015). https://doi.org/10.1007/s00138-014-0643-0

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  • DOI: https://doi.org/10.1007/s00138-014-0643-0

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