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Clustering Fiber Tr...
Clustering Fiber Traces Using Normalized Cuts
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- Brun, Anders, 1976- (author)
- Linköpings universitet,Medicinsk informatik,Tekniska högskolan,Centrum för medicinsk bildvetenskap och visualisering, CMIV
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- Knutsson, Hans, 1950- (author)
- Linköpings universitet,Medicinsk informatik,Tekniska högskolan,Centrum för medicinsk bildvetenskap och visualisering, CMIV
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- Park, Hae-Jeong (author)
- Clinical Neuroscience Division, Laboratory of Neuroscience, Boston VA, USA Health Care System-Brockton Division, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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- Shenton, Martha E. (author)
- Clinical Neuroscience Division, Laboratory of Neuroscience, Boston VA, USA Health Care System-Brockton Division, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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- Westin, Carl-Fredrik (author)
- Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA
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(creator_code:org_t)
- Berlin, Heidelberg : Springer Berlin/Heidelberg, 2004
- 2004
- English.
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In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540229766 - 9783540301356 ; , s. 368-375
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- In this paper we present a framework for unsupervised segmentation of white matter fiber traces obtained from diffusion weighted MRI data. Fiber traces are compared pairwise to create a weighted undirected graph which is partitioned into coherent sets using the normalized cut (Ncut) criterion. A simple and yet effective method for pairwise comparison of fiber traces is presented which in combination with the Ncut criterion is shown to produce plausible segmentations of both synthetic and real fiber trace data. Segmentations are visualized as colored stream-tubes or transformed to a segmentation of voxel space, revealing structures in a way that looks promising for future explorative studies of diffusion weighted MRI data.
Keyword
- MEDICINE
- MEDICIN
Publication and Content Type
- ref (subject category)
- kon (subject category)
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