Fiber Tract Clustering on Manifolds With Dual Rooted-Graphs

Tsai, Ah‐Lim; Westin, C.-F.; Hero, Alfred O.; Willsky, A. S.

doi:10.1109/cvpr.2007.383096

Cited by 35 publications

(28 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To assist with this, simplified visualizations have been proposed. Early methods cluster the fibers and display a small set of representatives for each cluster [46,65], or cluster outlines [11]. Bottger et al [4] extend the mean-shift bundling [32] to 3D connections between brain areas; since the input data is a graph rather than a trail-set, the deformations produced by bundling are not critical.…”

Section: Tensor Field Visualizationmentioning

confidence: 99%

Functional Decomposition for Bundled Simplification of Trail Sets

Hurter

Puechmorel

Nicol

et al. 2018

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization.

show abstract

Section: Tensor Field Visualizationmentioning

confidence: 99%

Functional Decomposition for Bundled Simplification of Trail Sets

Hurter

Puechmorel

Nicol

et al. 2018

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…For example, a fiber model for white matter may contain more than ten thousand fibers, making it difficult to derive useful insights from the dataset. A number of fiber clustering techniques [5,18,20,24,26] have been used to group similar fibers (i.e., fibers that lie close to one another and follow similar trajectories through the tensor field) into automatically representative fiber bundles. Graphical representations of such fiber bundles reduce the visual complexity of the dataset thereby facilitating a user's exploration of the data and allow him or her to more quickly gain insights into the structural integrity and connectivity of the fibers [6,23].…”

Section: Introductionmentioning

confidence: 99%

A Novel Interface for Interactive Exploration of DTI Fibers

Chen¹,

Ding²,

Zhang

et al. 2009

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-Visual exploration is essential to the visualization and analysis of densely sampled 3D DTI fibers in biological speciments, due to the high geometric, spatial, and anatomical complexity of fiber tracts. Previous methods for DTI fiber visualization use zooming, color-mapping, selection, and abstraction to deliver the characteristics of the fibers. However, these schemes mainly focus on the optimization of visualization in the 3D space where cluttering and occlusion make grasping even a few thousand fibers difficult. This paper introduces a novel interaction method that augments the 3D visualization with a 2D representation containing a low-dimensional embedding of the DTI fibers. This embedding preserves the relationship between the fibers and removes the visual clutter that is inherent in 3D renderings of the fibers. This new interface allows the user to manipulate the DTI fibers as both 3D curves and 2D embedded points and easily compare or validate his or her results in both domains. The implementation of the framework is GPUbased to achieve real-time interaction. The framework was applied to several tasks, and the results show that our method reduces the user's workload in recognizing 3D DTI fibers and permits quick and accurate DTI fiber selection.

show abstract

“…Manifold learning techniques [4] have been proposed in order to reduce dimensionality and do the clustering in the induced low-dimensional space. However most of these methods suffer from two shortcomings: they are unable to provide an explicit mapping from the high-dimensional space This work was partially supported by Association Française contre les Myopathies (AFM: http://www.afm-france.org) under the DTI-MUSCLE project.…”

Section: Introductionmentioning

confidence: 99%

Support vector driven Markov random fields towards DTI segmentation of the human skeletal muscle

Neji

Fleury

Deux

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

View full text Add to dashboard Cite

In this paper we propose a classification-based method towards the segmentation of diffusion tensor images. We use Support Vector Machines to classify diffusion tensors and we extend linear classification to the non linear case. To this end, we discuss and evaluate three different classes of kernels on the space of symmetric definite positive matrices that are well suited for the classification of tensor data. We impose spatial constraints by means of a Markov random field model that takes into account the result of SVM classification. Experimental results are provided for diffusion tensor images of human skeletal muscles. They demonstrate the potential of our method in discriminating the different muscle groups.

show abstract

Fiber Tract Clustering on Manifolds With Dual Rooted-Graphs

Abstract: We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs.

Cited by 35 publications

References 11 publications

Functional Decomposition for Bundled Simplification of Trail Sets

Functional Decomposition for Bundled Simplification of Trail Sets

A Novel Interface for Interactive Exploration of DTI Fibers

Support vector driven Markov random fields towards DTI segmentation of the human skeletal muscle

Contact Info

Product

Resources

About