Abstract
Due to small training sets, statistical shape models constrain often too
much the deformation in medical image segmentation. Hence, an artificial
enlargement of the training set has been proposed as a solution for the
problem. In this paper, the error sources in the statistical shape model based
segmentation were analyzed and the optimization processes were improved. The
method was evaluated with 3D cardiac MR volume data. The enlargement method
based on non-rigid movement produced good results – with 250 artificial modes,
the average error for four-chamber model was 2.11 mm when evaluated using 25
subjects.
| Original language | English |
|---|---|
| Title of host publication | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006 |
| Editors | Rasmus Larsen, Mads Nielsen, Jon Sporring |
| Place of Publication | Heidelberg |
| Publisher | Springer |
| Pages | 75-82 |
| Volume | 1 |
| ISBN (Electronic) | 978-3-540-44708-5 |
| ISBN (Print) | 978-3-540-44707-8 |
| DOIs | |
| Publication status | Published - 2006 |
| MoE publication type | A4 Article in a conference publication |
| Event | 9th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2006) - Copenhagen, Denmark Duration: 1 Oct 2006 → 6 Oct 2006 |
Publication series
| Series | Lecture Notes in Computer Science |
|---|---|
| Volume | 4190 |
| ISSN | 0302-9743 |
Conference
| Conference | 9th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2006) |
|---|---|
| Country/Territory | Denmark |
| City | Copenhagen |
| Period | 1/10/06 → 6/10/06 |
Funding
Project code: 1271
Keywords
- Statistical shape models
- segmentation
- MR images