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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lotjonen, J. Koikkalainen, J. Thurfjell, L. Rueckert, D. |
| Copyright Year | 2009 |
| Description | Author affiliation: VTT Technical Research Centre of Finland, Tampere, Finland (Lotjonen, J.; Koikkalainen, J.) || Imperial College London, UK (Rueckert, D.) || GE Healthcare, Medical Diagnostics R&D, Uppsala, Sweden (Thurfjell, L.) |
| Abstract | Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different techniques to select atlases in the multi-atlas segmentation. In addition, the expectation maximisation algorithm was used to combinemulti-atlas and intensity model information. The average similarity index obtained for six subcortical structures was 0.84. |
| Starting Page | 21 |
| Ending Page | 24 |
| File Size | 165430 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424439317 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2009.5192973 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Gray-scale Mutual information Image analysis Medical services Medical diagnosis Research and development Educational institutions Markov random fields Brain Segmentation Registration Atlases |
| Content Type | Text |
| Resource Type | Article |
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