Segmenting Images by Combining Selected Atlases on Manifold

  • Yihui Cao
  • Yuan Yuan
  • Xuelong Li
  • Baris Turkbey
  • Peter L. Choyke
  • Pingkun Yan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Atlas selection and combination are two critical factors affecting the performance of atlas-based segmentation methods. In the existing works, those tasks are completed in the original image space. However, the intrinsic similarity between the images may not be accurately reflected by the Euclidean distance in this high-dimensional space. Thus, the selected atlases may be away from the input image and the generated template by combining those atlases for segmentation can be misleading. In this paper, we propose to select and combine atlases by projecting the images onto a low-dimensional manifold. With this approach, atlases can be selected according to their intrinsic similarity to the patient image. A novel method is also proposed to compute the weights for more efficiently combining the selected atlases to achieve better segmentation performance. The experimental results demonstrated that our proposed method is robust and accurate, especially when the number of training samples becomes large.

Keywords

Patient Image Locality Preserve Projection Label Image Manifold Learning Nonlinear Dimensionality Reduction 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yihui Cao
    • 1
  • Yuan Yuan
    • 1
  • Xuelong Li
    • 1
  • Baris Turkbey
    • 2
  • Peter L. Choyke
    • 2
  • Pingkun Yan
    • 1
  1. 1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and PhotonicsXi’an Institute of Optics and Precision Mechanics, Chinese Academy of SciencesXi’anP.R. China
  2. 2.National Institutes of Health, National Cancer InstituteMolecular Imaging ProgramBethesdaUSA

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