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A Novel 3D Segmentation of Vertebral Bones from Volumetric CT Images Using Graph Cuts

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 5876))

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Abstract

Bone mineral density (BMD) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies (VBs). In this paper, we present a novel and fast 3D segmentation framework ofVBs in clinical CT images using the graph cuts method. The Matched filter is employed to detect theVB region automatically. In the graph cuts method, aVB (object) and surrounding organs (background) are represented using a gray level distribution models which are approximated by a linear combination of Gaussians (LCG) to better specify region borders between two classes (object and background). Initial segmentation based on the LCG models is then iteratively refined by using MGRF with analytically estimated potentials. In this step, the graph cuts is used as a global optimization algorithm to find the segmented data that minimize a certain energy function, which integrates the LCG model and the MGRF model. Validity was analyzed using ground truths of data sets (expert segmentation) and the European Spine Phantom (ESP) as a known reference. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.

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Author information

Authors and Affiliations

  1. Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, Louisville, KY, 40292

    Melih S. Aslan, Asem Ali, Ham Rara, Aly A. Farag & Rachid Fahmi

  2. Image Analysis, Inc, 1380 Burkesville St, Columbia, KY, 42728, USA

    Ben Arnold & Ping Xiang

Authors
  1. Melih S. Aslan

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  2. Asem Ali

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  3. Ham Rara

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  4. Ben Arnold

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  5. Aly A. Farag

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  6. Rachid Fahmi

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  7. Ping Xiang

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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

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

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, 338-8570, Japan

    Yoshinori Kuno

  6. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  7. Univ. of Zurich, Department of Informatics, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  8. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  9. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  10.  ,  

    Miguel L. Encarnação

  11. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  12. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Aslan, M.S.et al. (2009). A Novel 3D Segmentation of Vertebral Bones from Volumetric CT Images Using Graph Cuts. In: Bebis, G.,et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_49

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