Automatic knee segmentation from multi-contrast MR images
This thesis is devoted to developing methods for automatic knee segmentation from multi-contrast MR images which provide different contrasts between joint structures and help the separation of different structures. By exploiting the combined information of FS SPGR and IDEAL GRE water & fat image...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2013
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| Online Access: | http://hdl.handle.net/10356/52482 |
| _version_ | 1824453801680044032 |
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| author | Zhang, Kunlei |
| author2 | Pina Marziliano |
| author_facet | Pina Marziliano Zhang, Kunlei |
| author_sort | Zhang, Kunlei |
| collection | NTU |
| description | This thesis is devoted to developing methods for automatic knee segmentation from multi-contrast MR images which provide different contrasts between joint structures and help the separation of different structures. By exploiting the combined information of FS SPGR and IDEAL GRE water & fat images, a simple but reliable technique for automatic bone segmentation is proposed by using a threshold-based method followed by connected component labeling and distance transform. Next an automatic cartilage segmentation scheme is developed by using supervised classification with the incorporation of spatial dependencies. The choice of the classifier can be some powerful classification models such as SVM and ELM. The advantages of the developed scheme are achieved via effective incorporation of both a useful feature set and the ELM (or SVM) classification with spatial dependencies between neighboring voxels via a DRF framework. Also, the proposed cartilage segmentation scheme can be applied to the segmentation of other joint structures. For example, we perform the proposed method for automatic meniscus segmentation with meniscal searching region in a volume of interest. The automatic segmentations of knee bones, cartilages and menisci are evaluated on a comprehensive multi-contrast MRI database. The developed segmentation methods achieve good performance compared with gold standard segmentations. They also outperform the ones based on independent classifiers in terms of segmentation accuracy, and compare favorably with other state-of-the-art automatic knee segmentation methods. |
| first_indexed | 2025-02-19T03:12:12Z |
| format | Thesis |
| id | ntu-10356/52482 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:12:12Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/524822023-07-04T16:16:48Z Automatic knee segmentation from multi-contrast MR images Zhang, Kunlei Pina Marziliano School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems This thesis is devoted to developing methods for automatic knee segmentation from multi-contrast MR images which provide different contrasts between joint structures and help the separation of different structures. By exploiting the combined information of FS SPGR and IDEAL GRE water & fat images, a simple but reliable technique for automatic bone segmentation is proposed by using a threshold-based method followed by connected component labeling and distance transform. Next an automatic cartilage segmentation scheme is developed by using supervised classification with the incorporation of spatial dependencies. The choice of the classifier can be some powerful classification models such as SVM and ELM. The advantages of the developed scheme are achieved via effective incorporation of both a useful feature set and the ELM (or SVM) classification with spatial dependencies between neighboring voxels via a DRF framework. Also, the proposed cartilage segmentation scheme can be applied to the segmentation of other joint structures. For example, we perform the proposed method for automatic meniscus segmentation with meniscal searching region in a volume of interest. The automatic segmentations of knee bones, cartilages and menisci are evaluated on a comprehensive multi-contrast MRI database. The developed segmentation methods achieve good performance compared with gold standard segmentations. They also outperform the ones based on independent classifiers in terms of segmentation accuracy, and compare favorably with other state-of-the-art automatic knee segmentation methods. Doctor of Philosophy (EEE) 2013-05-09T04:57:51Z 2013-05-09T04:57:51Z 2013 2013 Thesis http://hdl.handle.net/10356/52482 en 144 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Zhang, Kunlei Automatic knee segmentation from multi-contrast MR images |
| title | Automatic knee segmentation from multi-contrast MR images |
| title_full | Automatic knee segmentation from multi-contrast MR images |
| title_fullStr | Automatic knee segmentation from multi-contrast MR images |
| title_full_unstemmed | Automatic knee segmentation from multi-contrast MR images |
| title_short | Automatic knee segmentation from multi-contrast MR images |
| title_sort | automatic knee segmentation from multi contrast mr images |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | http://hdl.handle.net/10356/52482 |
| work_keys_str_mv | AT zhangkunlei automatickneesegmentationfrommulticontrastmrimages |