Automatic generation of statistical pose and shape models for articulated joints
Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for s...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2013
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| Online Access: | https://eprints.nottingham.ac.uk/42106/ |
| _version_ | 1848796420508745728 |
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| author | Chen, Xin Graham, Jim Hutchinson, Charles Muir, Lindsay |
| author_facet | Chen, Xin Graham, Jim Hutchinson, Charles Muir, Lindsay |
| author_sort | Chen, Xin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity. |
| first_indexed | 2025-11-14T19:47:42Z |
| format | Article |
| id | nottingham-42106 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:47:42Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-421062020-05-04T16:39:24Z https://eprints.nottingham.ac.uk/42106/ Automatic generation of statistical pose and shape models for articulated joints Chen, Xin Graham, Jim Hutchinson, Charles Muir, Lindsay Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity. Institute of Electrical and Electronics Engineers 2013-10-11 Article PeerReviewed Chen, Xin, Graham, Jim, Hutchinson, Charles and Muir, Lindsay (2013) Automatic generation of statistical pose and shape models for articulated joints. IEEE Transactions on Medical Imaging, 33 (2). pp. 372-383. ISSN 1558-254X Wrist Carpal bones 3D image registration Segmentation Statistical pose model Statistical shape model Articulated joint http://ieeexplore.ieee.org/document/6630071/ doi:10.1109/TMI.2013.2285503 doi:10.1109/TMI.2013.2285503 |
| spellingShingle | Wrist Carpal bones 3D image registration Segmentation Statistical pose model Statistical shape model Articulated joint Chen, Xin Graham, Jim Hutchinson, Charles Muir, Lindsay Automatic generation of statistical pose and shape models for articulated joints |
| title | Automatic generation of statistical pose and shape models for articulated joints |
| title_full | Automatic generation of statistical pose and shape models for articulated joints |
| title_fullStr | Automatic generation of statistical pose and shape models for articulated joints |
| title_full_unstemmed | Automatic generation of statistical pose and shape models for articulated joints |
| title_short | Automatic generation of statistical pose and shape models for articulated joints |
| title_sort | automatic generation of statistical pose and shape models for articulated joints |
| topic | Wrist Carpal bones 3D image registration Segmentation Statistical pose model Statistical shape model Articulated joint |
| url | https://eprints.nottingham.ac.uk/42106/ https://eprints.nottingham.ac.uk/42106/ https://eprints.nottingham.ac.uk/42106/ |