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Chan-Vese Revisited: Relation to Otsu’s Method and a Parameter-Free Non-PDE Solution via Morphological Framework

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

Chan-Vese is an important and well-established segmentation method. However, it tends to be challenging to implement, including issues such as initialization problems and establishing the values of several free parameters. The paper presents a detailed analysis of Chan-Vese framework. It establishes a relation between the Otsu binarization method and the fidelity terms of Chan-Vese energy functional, allowing for intelligent initialization of the scheme. An alternative, fast, and parameter-free morphological segmentation technique is also suggested. Our experiments indicate the soundness of the proposed algorithm.

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Acknowledgements

The research received initial funding from the Israel Science Foundation – F.I.R.S.T. (Bikura) Individual Grant no. 644/08, as well as the Israel Science Foundation Grant no. 1457/13. It was also funded by the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 229418, and by an Early Israel grant (New Horizons project), Tel Aviv University. This study was also supported by a generous donation from Mr. Jacques Chahine, made through the French Friends of Tel Aviv University. Arie Shaus is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship. The kind assistance of Dr. Shirly Ben-Dor Evian, Ms. Sivan Einhorn, Ms. Shira Faigenbaum-Golovin, and Mr. Barak Sober is greatly appreciated.

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Authors and Affiliations

  1. Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel

    Arie Shaus & Eli Turkel

Authors
  1. Arie Shaus
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  2. Eli Turkel
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Correspondence to Arie Shaus .

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. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Shaus, A., Turkel, E. (2016). Chan-Vese Revisited: Relation to Otsu’s Method and a Parameter-Free Non-PDE Solution via Morphological Framework. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

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