Name
Abstract | ||
|---|---|---|
We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi- and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 121 3D MRI datasets in end-diastolic (end-systolic) phase demonstrates accuracy and robustness, with 2.45 mm (2.84 mm) mean deviation from manual segmentation. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.media.2004.06.015 | Medical Image Analysis |
Keywords | DocType | Volume |
Image post-processing,Segmentation,Deformable models,Shape models,Cardiac MRI | Journal | 8 |
Issue | ISSN | Citations |
3 | 1361-8415 | 83 |
PageRank | References | Authors |
4.29 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael R. Kaus | 1 | 100 | 9.41 |
| Jens Von Berg | 2 | 247 | 27.11 |
| Jürgen Weese | 3 | 129 | 7.25 |
| Wiro J. Niessen | 4 | 3821 | 380.78 |
| Vladimir Pekar | 5 | 261 | 24.85 |