Using Fourier Descriptor Features in the Classification of White Matter Fiber Tracts in DTI

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In this paper, we present a novel set of features which mainly comprises Fourier descriptors to facilitate the classification of white matter (WM) fiber tracts reconstructed from diffusion tensor imaging (DTI) tractography. Particularly, Fourier descriptors composing the feature vectors are different frequency terms of the discrete Fourier transform of the Euclidean coordinates of the step points of WM fiber tracts. This approach can effectively reduce the computing cost in WM fiber tract classification due to the decreased feature vector dimensionality. As integrals, Fourier descriptor features make the algorithm less sensitive to DTI noise. The fact that Fourier descriptors achieve spatial independent representation and normalization of WM fiber tracts makes it useful for group studies of WM fiber tracts. Specially, a new strategy for using Fourier descriptors is proposed. Experimental results with real datasets show that this technique is feasible and may be an alternative to existing approaches.