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Diseased Region Detection of Longitudinal Knee MRI Data

  • Chao Huang
  • Liang Shan
  • Cecil Charles
  • Marc Niethammer
  • Hongtu Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

Statistical analysis of longitudinal cartilage changes in osteoarthritis (OA) is of great importance and still a challenge in knee MRI data analysis. A major challenge is to establish a reliable correspondence across subjects within the same latent subpopulations. We develop a novel Gaussian hidden Markov model (GHMM) to establish spatial correspondence of cartilage thinning across both time and subjects within the same latent subpopulations and make statistical inference on the detection of diseased regions in each OA patient. A hidden Markov random field (HMRF) is proposed to extract such latent subpopulation structure. The EM algorithm and pseudo-likelihood method are both considered in making statistical inference. The proposed model can effectively detect diseased regions and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Simulation studies and diseased region detection on 2D thickness maps extracted from full 3D longitudinal knee MRI Data for Pfizer Longitudinal Dataset are performed, which show that our proposed model outperforms standard voxel-based analysis.

Keywords

Diseased regions detection EM algorithm Gaussian hidden Markov model Longitudinal cartilage thickness Pseudo-likelihood method 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chao Huang
    • 1
    • 3
  • Liang Shan
    • 2
  • Cecil Charles
    • 4
  • Marc Niethammer
    • 2
  • Hongtu Zhu
    • 3
  1. 1.Department of MathematicsSoutheast UniversityChina
  2. 2.Department of Computer Sciences and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillUSA
  3. 3.Department of Biostatistics and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillUSA
  4. 4.Department of RadiologyDuke UniversityUSA

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