Difference between revisions of "Projects:TopologyCorrectionNonSeparatingLoops"
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| + | = Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops = | ||
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Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]] | Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]] | ||
| − | ''' | + | '''Objective''' |
| + | |||
| + | Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops | ||
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| + | '''Progress''' | ||
| − | '' | + | ''Status'': Prototype |
| − | + | ''Submitted'': IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007 | |
| − | '' | + | ''Abstract'': In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically,we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator. |
| − | ''' | + | '''Key Investigators''' |
| − | + | * Florent Ségonne, Jenni Pacheco, and Bruce Fischl | |
Revision as of 01:23, 4 September 2007
Home < Projects:TopologyCorrectionNonSeparatingLoopsGeometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops
Back to NA-MIC_Collaborations
Objective
Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops
Progress
Status: Prototype
Submitted: IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007
Abstract: In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically,we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator.
Key Investigators
- Florent Ségonne, Jenni Pacheco, and Bruce Fischl
