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Automatic and Reliable Segmentation of Spinal Canals in Low-Resolution, Low-Contrast CT Images

  • Qian WangEmail author
  • Le Lu
  • Dijia Wu
  • Noha El-Zehiry
  • Dinggang Shen
  • Kevin S. Zhou
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 17)

Abstract

Accurate segmentation of spinal canals in Computed Tomography (CT) images is an important task in many related studies. In this paper, we propose an automatic segmentation method and apply it to our highly challenging 110 datasets from the CT channel of PET-CT scans. We adapt the interactive random-walks (RW) segmentation algorithm to be fully automatic which is initialized with robust voxelwise classification using Haar features and probabilistic boosting tree. One-shot RW is able to estimate yet imperfect segmentation. We then refine the topology of the segmented spinal canal leading to improved seeds or boundary conditions of RW. Therefore, by iteratively optimizing the spinal canal topology and running RW segmentation, satisfactory segmentation results can be acquired within only a few iterations. Our experiments validate the capability of the proposed method with promising segmentation performance, even though the resolution and the contrast of our datasets are low.

Keywords

Random Walk Spinal Canal Segmentation Result Medial Line Medial Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Qian Wang
    • 1
    • 4
    Email author
  • Le Lu
    • 2
  • Dijia Wu
    • 3
  • Noha El-Zehiry
    • 3
  • Dinggang Shen
    • 4
  • Kevin S. Zhou
    • 3
  1. 1.Department of Computer Science, Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Radiology and Imaging ScienceNational Institutes of Health (NIH) Clinical CenterBethesdaUSA
  3. 3.Siemens Corporate ResearchPrincetonUSA
  4. 4.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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