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Automatic Segmentation of Extraocular Muscles Using Superpixel and Normalized Cuts

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Advances in Visual Computing (ISVC 2015)

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

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Abstract

This paper proposes a novel automatic method to segment extraocular muscles and orbital structures. Instead of conventional segmentation at the pixel level, superpixels at the structure level were used as the basic image processing unit. A region adjacency graph was built based on the neighborhood relationship among superpixels. Using Normalized Cuts on the region adjacency graph, we refined the segmentation by using a variety of features derived from the classical shape cues, including contours and continuity. To demonstrate the efficiency of the method, segmentation of Magnetic Resonance images of five healthy subjects was performed and analyzed. Three region-based image segmentation evaluation metrics were applied to quantify the automatic segmentation accuracy against manual segmentation. Our novel method could produce accurate and reproducible eye muscle segmentation.

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Acknowledgments

Supported by the Jeffress Trust Awards, NIH grant EY08313 and an unrestricted grant from Research to Prevent Blindness.

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Authors and Affiliations

  1. Department of Computer Science, George Mason University, Fairfax, VA, USA

    Qi Xing

  2. Lake Braddock Secondary School, Burke, VA, USA

    Yifan Li

  3. Department of Bioengineering, George Mason University, Fairfax, VA, USA

    Brendan Wiggins & Qi Wei

  4. Department of Neurology, Jules Stein Eye Institute, David Geffen Medical School at University of California, Los Angeles, USA

    Joseph L. Demer

Authors
  1. Qi Xing
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  2. Yifan Li
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  3. Brendan Wiggins
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  4. Joseph L. Demer
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  5. Qi Wei
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Corresponding author

Correspondence to Qi Wei .

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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Xing, Q., Li, Y., Wiggins, B., Demer, J.L., Wei, Q. (2015). Automatic Segmentation of Extraocular Muscles Using Superpixel and Normalized Cuts. 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_45

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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