2D rodent brain extraction using shape model and template learning
Accurate rodent brain extraction is the basic step for many translational study using MR imaging. This report presents a template based approach to automatic rodent brain extraction. We first build the brain appearance model based on the learning exemplars. Together with the template matching, we en...
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sg-ntu-dr.10356-675802023-07-07T17:55:14Z 2D rodent brain extraction using shape model and template learning Zhang, Jiaqi Lin Zhiping School of Electrical and Electronic Engineering A*STAR Institute for Infocomm Research DRNTU::Engineering Accurate rodent brain extraction is the basic step for many translational study using MR imaging. This report presents a template based approach to automatic rodent brain extraction. We first build the brain appearance model based on the learning exemplars. Together with the template matching, we encode the rodent brain position into the search space to reliably locate the rodent brain and estimate the rough segmentation. With the initial mask, a level-set segmentation and a mask-based template learning are implemented further in the brain region. The fusion of the experts is used to generate a new mask. We finally combine the region growing based on the histogram distribution learning to delineate the final brain mask. Tested on a public data set, we achieved favorable results in both the automatic brain localization and segmentation. Bachelor of Engineering 2016-05-18T06:01:30Z 2016-05-18T06:01:30Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67580 en Nanyang Technological University 48 p. application/pdf |
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DRNTU::Engineering Zhang, Jiaqi 2D rodent brain extraction using shape model and template learning |
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Accurate rodent brain extraction is the basic step for many translational study using MR imaging. This report presents a template based approach to automatic rodent brain extraction. We first build the brain appearance model based on the learning exemplars. Together with the template matching, we encode the rodent brain position into the search space to reliably locate the rodent brain and estimate the rough segmentation. With the initial mask, a level-set segmentation and a mask-based template learning are implemented further in the brain region. The fusion of the experts is used to generate a new mask. We finally combine the region growing based on the histogram distribution learning to delineate the final brain mask. Tested on a public data set, we achieved favorable results in both the automatic brain localization and segmentation. |
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Lin Zhiping |
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Lin Zhiping Zhang, Jiaqi |
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Final Year Project |
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Zhang, Jiaqi |
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Zhang, Jiaqi |
title |
2D rodent brain extraction using shape model and template learning |
title_short |
2D rodent brain extraction using shape model and template learning |
title_full |
2D rodent brain extraction using shape model and template learning |
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2D rodent brain extraction using shape model and template learning |
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2D rodent brain extraction using shape model and template learning |
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2d rodent brain extraction using shape model and template learning |
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2016 |
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http://hdl.handle.net/10356/67580 |
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1772827855784247296 |