White matter fiber tract segmentation in DT-MRI using geometric flows

Jonasson, Lisa; Bresson, Xavier; Hagmann, Patric; Cuisenaire, O.; Meuli, Reto; Thiran, Jean-Philippe

doi:10.1016/j.media.2004.07.004

Cited by 70 publications

(70 citation statements)

References 46 publications

(50 reference statements)

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“…Another class of measures use the fact that we have matrices. Pierpaoli and Basser [13] propose to use the sum of the squared vector dot products of the eigenvectors weighted by the product of the eigenvalues as a tensor scalar product [7]: s tsp includes the colinearity of the orientation of the tensors weighted by its eigenvalues. The value is maximized if the tensors are aligned.…”

Section: Linear Algebramentioning

confidence: 99%

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Analysis of Distance/Similarity Measures for Diffusion Tensor Imaging

Peeters¹,

Rodrigues²,

Vilanova³

et al. 2009

Mathematics and Visualization

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PrefaceThis book attempts to capture some of the excitement of an inspiring Dagstuhl Seminar in January 2007. The authors report on recent research results as well as opining on future directions for the analysis and visualization of tensor fields. Topics range from applications of the analysis of tensor fields to purer research into their mathematical and analytical properties. One of the goals of this seminar was to bring together researchers from along that pure-to-applied disciplinary axis with the hope of fostering new collaborations and research. This book, we hope, will continue to further that goal in a broader context. Summary. Many different measures have been proposed to compute similarities and distances between diffusion tensors. These measures are commonly used for algorithms such as segmentation, registration, and quantitative analysis of Diffusion Tensor Imaging data sets. The results obtained from these algorithms are extremely dependent on the chosen measure. The measures presented in literature can be of complete different nature, and it is often difficult to predict the behavior of a given measure for a specific application. In this chapter, we classify and summarize the different measures that have been presented in literature. We also present a framework to analyze and compare the behavior of the measures according to several selected properties. We expect that this framework will help in the initial selection of a measure for a given application and to identify when the generation of a new measure is needed. This framework will also allow the comparison of new measures with existing ones.

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Section: Linear Algebramentioning

confidence: 99%

“…4.6. Jonasson et al [7] use the normalized tensor scalar product s ntsp in order to make it invariant to scaling of the tensors:…”

Section: Linear Algebramentioning

confidence: 99%

Analysis of Distance/Similarity Measures for Diffusion Tensor Imaging

Peeters¹,

Rodrigues²,

Vilanova³

et al. 2009

Mathematics and Visualization

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show abstract

“…Other methods for tensor image regularization [4][5][6][7] and segmentation [8][9][10][11] have been suggested. However, all of them use a fixed distance measure on the tensors.…”

Section: Introductionmentioning

confidence: 99%

Flexible Segmentation and Smoothing of DT-MRI Fields Through a Customizable Structure Tensor

Schultz

Burgeth

Weickert

2006

Advances in Visual Computing

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Abstract. We present a novel structure tensor for matrix-valued images. It allows for user defined parameters that add flexibility to a number of image processing algorithms for the segmentation and smoothing of tensor fields. We provide a thorough theoretical derivation of the new structure tensor, including a proof of the equivalence of its unweighted version to the existing structure tensor from the literature. Finally, we demonstrate its advantages for segmentation and smoothing, both on synthetic tensor fields and on real DT-MRI data.

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“…In designing the speed term, we follow a similar approach to that presented in [9]. As mentioned earlier, our front propagation method is based on the idea that voxels with similar temporal behavior belong to the same functional region in the brain.…”

Section: Speed Function-mentioning

confidence: 99%

Level Set Based Clustering for Analysis of Functional Mri Data

Bathula

Papademetris

Duncan

2007

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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We present a level set based clustering technique to detect activation regions from functional brain images using contextual information. Earlier similar approaches have been primarily concerned with local spatial context. Our approach relies on the idea that voxels within a functional region have similar temporal behavior. Using a level set formulation, a two-dimensional curve is evolved with a speed proportional to a similarity measure between the fMRI signals of voxels lying on the curve and their neighbors in the direction of propagation. The correlation coefficient is used to quantify similarity in time series of adjacent voxels. Simulation results from synthetic images demonstrate that using spatio-temporal contextual information provides better segmentation than a context-free, voxel-wise technique. Results from a real fMRI experiment using auditory stimulation are also presented.

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White matter fiber tract segmentation in DT-MRI using geometric flows

Cited by 70 publications

References 46 publications

Analysis of Distance/Similarity Measures for Diffusion Tensor Imaging

Analysis of Distance/Similarity Measures for Diffusion Tensor Imaging

Flexible Segmentation and Smoothing of DT-MRI Fields Through a Customizable Structure Tensor

Level Set Based Clustering for Analysis of Functional Mri Data

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