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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liang Shan Charles, C. Niethammer, M. |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Radiology, Duke University, Durham, NC, USA (Charles, C.) || Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA (Liang Shan; Niethammer, M.) |
| Abstract | In this paper, we propose a multi-atlas-based method to automatically segment the femoral and tibial cartilage from T1 weighted magnetic resonance (MR) knee images. The segmentation result is a joint decision of the spatial priors from a multi-atlas registration and the local likelihoods within a Bayesian framework. The cartilage likelihoods are obtained from a probabilistic k nearest neighbor classification. Validation results on 18 knee MR images against the manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 75.2% and 81.7% respectively). |
| Starting Page | 1028 |
| Ending Page | 1031 |
| File Size | 657493 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457718571 |
| ISSN | 19457928 |
| e-ISBN | 9781457718588 |
| DOI | 10.1109/ISBI.2012.6235733 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-02 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Bones Joints Probabilistic logic Training Biomedical imaging Magnetic resonance MR Multi-atlas segmentation registration probabilistic k nearest neighbor cartilage bone knee |
| Content Type | Text |
| Resource Type | Article |
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