A Novel Criterion for Characterizing Diffusion Anisotropy in HARDI Data Based on the MDL Technique

  • H. Z. Zhang
  • T. M. McGinnity
  • S. A. Coleman
  • M. Jing
Conference paper

DOI: 10.1007/978-3-642-13923-9_44

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6165)
Cite this paper as:
Zhang H.Z., McGinnity T.M., Coleman S.A., Jing M. (2010) A Novel Criterion for Characterizing Diffusion Anisotropy in HARDI Data Based on the MDL Technique. In: Zhang D., Sonka M. (eds) Medical Biometrics. ICMB 2010. Lecture Notes in Computer Science, vol 6165. Springer, Berlin, Heidelberg

Abstract

Based on the spherical harmonic decomposition of HARDI data, we propose a new criterion for characterizing the diffusion anisotropy in a voxel directly from the SH coefficients. Essentially, by considering the Rician noise in diffusion data, we modify the Rissanen’s criterion for fitting the diffusion situation in a voxel. In addition, the minimum description length (MDL) criterion has been employed for interpreting information from both the SH coefficients and the data. The criterion obtained can make use of the diffusion information so as to efficiently separate the different diffusion distributions. Various synthetic datasets have been used for verifying our method. The experimental results show the performance of the proposed criterion is accurate.

Keywords

HARDI Spherical harmonic MDL Diffusion anisotropy 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • H. Z. Zhang
    • 1
  • T. M. McGinnity
    • 1
  • S. A. Coleman
    • 1
  • M. Jing
    • 1
  1. 1.Intelligent Systems Research CentreUniversity of Ulster at MageeNorthern Ireland, UK

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