Difference between revisions of "Algorithm:Past Featured Articles"
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* <div id="pohl_ni_06">'''K. M. Pohl et al. [[Media:Pohl-NeuroImage-06.pdf| A Bayesian Model for Joint Segmentation and Registration]]'''</div> | * <div id="pohl_ni_06">'''K. M. Pohl et al. [[Media:Pohl-NeuroImage-06.pdf| A Bayesian Model for Joint Segmentation and Registration]]'''</div> | ||
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| + | | rowspan="3" width="100px" align="center" valign="top" | | ||
| + | [[Image:Pohl_reg_seg_mov.gif|[[Image:Pohl_reg_seg_mov.gif|Joint Registration and Segmentation Example]]]] | ||
| + | | valign="top" | A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation. | ||
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| + | K. M. Pohl, J. Fisher, W.E.L. Grimson, R. Kikinis, and W.M. Wells, [[Media:Pohl-NeuroImage-06.pdf| A Bayesian Model for Joint Segmentation and Registration]]. NeuroImage, 31(1):228-239, 2006. | ||
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| + | * <div id="nain_tmi_07">'''D. Nain et al. [[Media:Nain07-spherical-wavelets.pdf| ]]'''</div> | ||
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Revision as of 16:34, 2 August 2007
Home < Algorithm:Past Featured Articles- K. M. Pohl et al. A Bayesian Model for Joint Segmentation and Registration
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A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation. |
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K. M. Pohl, J. Fisher, W.E.L. Grimson, R. Kikinis, and W.M. Wells, A Bayesian Model for Joint Segmentation and Registration. NeuroImage, 31(1):228-239, 2006. |
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A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation. |
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K. M. Pohl, J. Fisher, W.E.L. Grimson, R. Kikinis, and W.M. Wells, A Bayesian Model for Joint Segmentation and Registration. NeuroImage, 31(1):228-239, 2006. |
