A novel criterion for characterizing diffusion anisotropy in HARDI data based on the MDL technique

Authors:
H. Z. Zhang;T. M. McGinnity;S. A. Coleman;M. Jing
Affiliations:
Intelligent Systems Research Centre, University of Ulster at Magee, Northern Ireland, UK;Intelligent Systems Research Centre, University of Ulster at Magee, Northern Ireland, UK;Intelligent Systems Research Centre, University of Ulster at Magee, Northern Ireland, UK;Intelligent Systems Research Centre, University of Ulster at Magee, Northern Ireland, UK
Venue:
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Year:
2010

Citing 2
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Apparent diffusion coefficient approximation and diffusion anisotropy characterization in DWI

IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
MDL denoising

IEEE Transactions on Information Theory

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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.