Generalized hardi invariants by method of tensor contraction

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Publication Type pre-print
School or College <blank>
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Creator Gur, Yaniv
Other Author Johnson, Chris R.
Title Generalized hardi invariants by method of tensor contraction
Date 2014-01-01
Description We propose a 3D object recognition technique to construct rotation invariant feature vectors for high angular resolution diffusion imaging (HARDI). This method uses the spherical harmonics (SH) expansion and is based on generating rank-1 contravariant tensors using the SH coefficients, and contracting them with covariant tensors to obtain invariants. The proposed technique enables the systematic construction of invariants for SH expansions of any order using simple mathematical operations. In addition, it allows construction of a large set of invariants, even for low order expansions, thus providing rich feature vectors for image analysis tasks such as classification and segmentation. In this paper, we use this technique to construct feature vectors for eighth-order fiber orientation distributions (FODs) reconstructed using constrained spherical deconvolution (CSD). Using simulated and in vivo brain data, we show that these invariants are robust to noise, enable voxel-wise classification, and capture meaningful information on the underlying white matter structure.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 718
Last Page 721
Language eng
Bibliographic Citation Gur, Y., & Johnson, C. R. (2014). Generalized hardi invariants by method of tensor contraction. 2014 IEEE 11th International Symposium on Biomedical Imaging, 718-21.
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Identifier uspace,19410
ARK ark:/87278/s6962spm
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Date Created 2015-05-04
Date Modified 2015-05-04
ID 713034
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