Co-registration of White Matter Tractographies by Adaptive-Mean-Shift and Gaussian Mixture Modeling

Zvitia, Orly; Mayer, Arnaldo; Shadmi, Ran; Miron, Sébastian; Greenspan, Hayit

doi:10.1109/tmi.2009.2029097

Cited by 31 publications

(21 citation statements)

References 44 publications

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“…Recently, Durrleman et al (2014) improved and extended this framework on currents by using varifolds. Zvitia et al (2008Zvitia et al ( , 2010 used adaptive mean shift clustering to extract a number of representative fiber-modes. Each fiber mode was assigned to a multivariate Gaussian distribution according to its population thereby leading to a Gaussian mixture model (GMM) representation for the entire set of fascicles.…”

Section: Streamline-basedmentioning

confidence: 99%

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

et al. 2015

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Section: Streamline-basedmentioning

confidence: 99%

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

et al. 2015

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“…The choice of k is supported by previous research [10] which concluded this number to be good compromise between the complexity and fidelity to the original representation. The details of the different approaches to fiber reduction are given next.…”

Section: Methodsmentioning

confidence: 99%

Coresets vs clustering: comparison of methods for redundancy reduction in very large white matter fiber sets

et al. 2016

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Recent advances in Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) of white matter in conjunction with improved tractography produce impressive reconstructions of White Matter (WM) pathways. These pathways (fiber sets) often contain hundreds of thousands of fibers, or more. In order to make fiber based analysis more practical, the fiber set needs to be preprocessed to eliminate redundancies and to keep only essential representative fibers. In this paper we demonstrate and compare two distinctive frameworks for selecting this reduced set of fibers. The first framework entails pre-clustering the fibers using k-means, followed by Hierarchical Clustering and replacing each cluster with one representative. For the second clustering stage seven distance metrics were evaluated. The second framework is based on an efficient geometric approximation paradigm named coresets. Coresets present a new approach to optimization and have huge success especially in tasks requiring large computation time and/or memory. We propose a modified version of the coresets algorithm, Density Coreset. It is used for extracting the main fibers from dense datasets, leaving a small set that represents the main structures and connectivity of the brain. A novel approach, based on a 3D indicator structure, is used for comparing the frameworks. This comparison was applied to High Angular Resolution Diffusion Imaging (HARDI) scans of 4 healthy individuals. We show that among the clustering based methods, that cosine distance gives the best performance. In comparing the clustering schemes with coresets, Density Coreset method achieves the best performance.

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“…So far, methods that have performed registration using unlabeled fiber tracts from the whole brain, e.g. [9, 21, 12], have been limited to subject-to-subject (pairwise) registration.…”

Section: Introductionmentioning

confidence: 99%

Unbiased Groupwise Registration of White Matter Tractography

O’Donnell

Wells

Golby

et al. 2012

Lecture Notes in Computer Science

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Abstract.We present what we believe to be the first investigation into unbiased multi-subject registration of whole brain diffusion tractography of the white matter. To our knowledge, this is also the first entropybased objective function applied to fiber tract registration. To define the probability of fiber trajectories for the computation of entropy, we take advantage of a pairwise fiber distance used as the basis for a Gaussianlike kernel. By employing several values of the kernel's scale parameter, the method is inherently multi-scale. Results of experiments using synthetic and real datasets demonstrate the potential of the method for simultaneous joint registration of tractography.

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Co-registration of White Matter Tractographies by Adaptive-Mean-Shift and Gaussian Mixture Modeling

Cited by 31 publications

References 44 publications

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

Coresets vs clustering: comparison of methods for redundancy reduction in very large white matter fiber sets

Unbiased Groupwise Registration of White Matter Tractography

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