Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data

Zhukov, Leonid; Museth, Ken; Breen, David E.; Barr, Alan H.; Whitaker, Ross T.

doi:10.1117/1.1527628

Cited by 84 publications

(54 citation statements)

References 32 publications

(23 reference statements)

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“…Using tractography (e.g., see Vilanova et al [15]) it is possible to reconstruct connections in the brain or the fibrous structure of muscle tissue such as the heart (e.g., see Zhukov et al [19]). In several applications, for example, comparison between subjects, it is interesting to segment structures with a higher level of meaning, for example, white matter bundles, that is [14,16,20], and also to register different DTI data sets [1,10,18]. It is often also necessary to derive statistical properties of diffusion tensors (DTs) to identify differences, for example, between healthy and pathology areas [11].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

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

“…Here, as an example, we have adapted the hidden Markov random field formalism to regularize and segment the data. In this sense, our approach is not very different to the idea of (Zhukov et al, 2003) that applied segmentation to DTI in order to separate the white matter from the remaining gray matter and cerebro-spinal fluid using fractional anisotropy images. However, as orientational information of the DT is not used, no specific tract can be identified with its method.…”

Section: Discussionmentioning

confidence: 99%

Fibertract segmentation in position orientation space from high angular resolution diffusion MRI

et al. 2006

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In diffusion MRI, standard approaches for fibertract identification are based on algorithms that generate lines of coherent diffusion, currently known as tractography. A tract is then identified as a set of such lines selected on some criteria. In the present study, we investigate whether fibertract identification can be formulated as a segmentation task that recognizes a fibertract as a region where diffusion is intense and coherent. Indeed, we show that it is possible to segment efficiently wellknown fibertracts with classical image processing methods provided that the problem is formulated in a five-dimensional space of position and orientation. As an example, we choose to adapt to this newly defined high-dimensional non-Euclidean space, called position orientation space, an algorithm based on the hidden Markov random field framework. Structures such as the cerebellar peduncles, corticospinal tract, association bundles can be identified and represented in three dimensions by a back projection technique similar to maximum intensity projection. Potential advantages and drawbacks as compared to classical tractography are discussed; for example, it appears that our formulation handles naturally crossing tracts and is not biased by human intervention. D

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“…The practicality of the split and merge technique is limited because it does not describe the complete fiber pathway. Segmentation approaches of DTI [37,40,14] are more suitable for identifying coherent densely packed bundles of axons. The segmentation avoids the drawbacks from both connectivity maps and tractography such as tracking accumulation errors and the need to merge the individual tracts to obtain fiber bundles.…”

Section: Introductionmentioning

confidence: 99%