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Fuzzy Skeletonization Improves the Performance of Characterizing Trabecular Bone Micro-architecture

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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

Skeletonization provides a compact, yet effective representation of an object. Despite limited resolution, most medical imaging applications till date use binary skeletonization which is always associated with thresholding related data loss. A recently-developed fuzzy skeletonization algorithm directly operates on fuzzy objects in the presence of partially volumed voxels and alleviates this data loss. In this paper, the performance of fuzzy skeletonization is examined in a popular biomedical application of characterizing human trabecular bone (TB) plate/rod micro-architecture under limited resolution and compared with a binary method. Experimental results have shown that, using the volumetric topological analysis, fuzzy skeletonization leads to more accurate and reproducible measure of TB plate-width than the binary method. Also, fuzzy skeletonization-based plate-width measure showed a stronger linear association (\(R^{2} = 0.92\)) with the actual bone strength than the binary skeletonization-based measure.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USA

    Cheng Chen, Dakai Jin & Punam K. Saha

  2. Department of Radiology, University of Iowa, Iowa City, USA

    Punam K. Saha

Authors
  1. Cheng Chen
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  2. Dakai Jin
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  3. Punam K. Saha
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Corresponding author

Correspondence to Cheng Chen .

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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Chen, C., Jin, D., Saha, P.K. (2015). Fuzzy Skeletonization Improves the Performance of Characterizing Trabecular Bone Micro-architecture. 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_2

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_2

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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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