Shape modeling and clustering of white matter fiber tracts using fourier descriptors

Abstract

Abstract-Reliable shape modeling and clustering of white matter fiber tracts is essential for clinical and anatomical studies that use diffusion tensor imaging (DTI) tractography techniques. In this work we present a novel scheme to model the shape of white matter fiber tracts reconstructed from DTI and cluster them into bundles using Fourier descriptors. We characterize a tract's shape by using Fourier descriptors which are effective in capturing shape properties of fiber tracts. Fourier descriptors derived from different shape signatures are analyzed. Clustering is then performed on these multidimensional features in conjunction with mass centers using a k-means like threshold based approach. The advantage of this method lies in the fact that Fourier descriptors achieve spatial independent representation and normalization of white matter fiber tracts which makes it useful for tract comparison across subjects. It also eliminates the need to find matching correspondences between two randomly organized tracts from whole brain tracking. Several issues related to tract shape representation and normalization are also discussed. Real DTI datasets are used to test this technique. Experiment results show that this technique can effectively separate multiple fascicles into plausible bundles.

Population analysis of the cingulum bundle using the tubular surface model for schizophrenia detection

“…In this work, we use the following features to describe the CB: mean curvature, geometric curvature, normalized cross-sectional area, center-shifted coordinates (as defined in 15 ), Fractional Anisotropy (FA) and Mean Diffusion (MD). FA and MD characterize the WM properties while mean curvature, geometric curvature and the center-shifted coordinates encode shape information.…”

Shape annotation for intelligent image retrieval

“…Each cluster is then represented by a prototype or template and manually associated to linguistic concepts. Examples of this approach can be found in [12,25,27,29,40]. However, unsupervised clustering methods often generate inconsistent clusters including shapes that, although visually similar, actually represent different linguistic concepts (an example is given in Fig.…”