Book ChapterDOI
Coloring of DT-MRI Fiber Traces Using Laplacian Eigenmaps
Anders Brun,Hae-Jeong Park,Hans Knutsson,Carl-Fredrik Westin +3 more
- Vol. 2809, pp 518-529
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TLDR
It is demonstrated that when the low-dimensional space is the RGB color space, this can be used to visualize fiber traces in a way which enhances the perception of fiber bundles and connectivity in the human brain.Abstract:
We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian eigenmaps [3], we create a mapping from a set of fiber traces to a low dimensional Euclidean space. Laplacian eigenmaps constructs this mapping so that similar traces are mapped to similar points, given a custom made pairwise similarity measure for fiber traces. We demonstrate that when the low-dimensional space is the RGB color space, this can be used to visualize fiber traces in a way which enhances the perception of fiber bundles and connectivity in the human brain.read more
Citations
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Dimensionality Reduction: A Comparative Review
TL;DR: The results of the experiments reveal that nonlinear techniques perform well on selected artificial tasks, but that this strong performance does not necessarily extend to real-world tasks.
Journal ArticleDOI
Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas
TL;DR: A new white matter atlas creation method that learns a model of the common white matter structures present in a group of subjects, enabling group comparison of white matter anatomy and results regarding the stability of the method and parameter choices are presented.
Journal ArticleDOI
Incremental nonlinear dimensionality reduction by manifold learning
M.H.C. Law,Anil K. Jain +1 more
TL;DR: An incremental version of ISOMAP, one of the key manifold learning algorithms, is described and it is demonstrated that this modified algorithm can maintain an accurate low-dimensional representation of the data in an efficient manner.
Book ChapterDOI
Clustering Fiber Traces Using Normalized Cuts.
Anders Brun,Hans Knutsson,Hae-Jeong Park,Hae-Jeong Park,Martha E. Shenton,Carl-Fredrik Westin +5 more
TL;DR: A simple and yet effective method for pairwise comparison of fiber traces is presented which in combination with the N cut criterion is shown to produce plausible segmentations of both synthetic and real fiber trace data.
Book
Visualization in Medicine: Theory, Algorithms, and Applications
Bernhard Preim,Dirk Bartz +1 more
TL;DR: Visualization in Medicine is the first book on visualization and its application to problems in medical diagnosis, education, and treatment and describes the algorithms, the applications and their validation, and the clinical evaluation (are the techniques useful?).
References
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Journal ArticleDOI
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TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Proceedings Article
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Mikhail Belkin,Partha Niyogi +1 more
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Journal ArticleDOI
In vivo fiber tractography using DT-MRI data
TL;DR: Fiber tract trajectories in coherently organized brain white matter pathways were computed from in vivo diffusion tensor magnetic resonance imaging (DT‐MRI) data, and the method holds promise for elucidating architectural features in other fibrous tissues and ordered media.
Journal ArticleDOI
Virtual in vivo interactive dissection of white matter fasciculi in the human brain
TL;DR: The use of diffusion tensor magnetic resonance tractography to visualize the three-dimensional structure of the major white matter fasciculi within living human brain adds a new dimension to anatomical descriptions of the living humanbrain.
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