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Segmentation of Partially Overlapping Nanoparticles Using Concave Points

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

This paper presents a novel method for the segmentation of partially overlapping nanoparticles with a convex shape in silhouette images. The proposed method involves two main steps: contour evidence extraction and contour estimation. Contour evidence extraction starts with contour segmentation where contour segments are recovered from a binarized image by detecting concave points. After this, contour segments which belong to the same object are grouped by utilizing properties of fitted ellipses. Finally, the contour estimation is implemented through a non-linear ellipse fitting problem in which partially observed objects are modeled in the form of ellipse-shape objects. The experiments on a dataset consisting of nanoparticles demonstrate that the proposed method outperforms two current state-of-art approaches in overlapping nanoparticles segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have an approximately elliptical shape, such as cell segmentation.

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Author information

Authors and Affiliations

  1. Machine Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta University of Technology, Lappeenranta, Finland

    Sahar Zafari, Tuomas Eerola & Heikki Kälviäinen

  2. Mathematics Laboratory, School of Engineering Science, Lappeenranta University of Technology, Lappeenranta, Finland

    Jouni Sampo & Heikki Haario

  3. School of Information Technology, Monash University Malaysia, Selangor, Malaysia

    Heikki Kälviäinen

Authors
  1. Sahar Zafari

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  2. Tuomas Eerola

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  3. Jouni Sampo

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  4. Heikki Kälviäinen

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  5. Heikki Haario

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Corresponding author

Correspondence toSahar Zafari.

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Zafari, S., Eerola, T., Sampo, J., Kälviäinen, H., Haario, H. (2015). Segmentation of Partially Overlapping Nanoparticles Using Concave Points. In: Bebis, G.,et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_17

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Chapter
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