Name
Abstract | ||
|---|---|---|
The shape propagation scheme robustly combines shape and edge information in two steps to perform volumetric image segmentation. The inward-propagating step performs shape interpolation from user-defined sparse segmentations. The edge estimation step improves the accuracy of interpolated boundaries using a Bayesian approach that handles the presence of edges or its lack of. The scheme was found to be robust in segmenting T-1 weighted MRI of the corpus callosum. The algorithm also runs in linear time. The efficiency and robustness of this scheme demonstrates significant potential for use in assisting tedious manual volumetric segmentation that may be performed in clinical applications |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ISBI.2006.1625022 | Arlington, VA |
Keywords | Field | DocType |
Bayes methods,biomedical MRI,image segmentation,interpolation,medical image processing,Bayesian approach,T-1 weighted MRI,corpus callosum,edge estimation,intensity-based shape propagation,shape interpolation,user-defined sparse segmentations,volumetric image segmentation | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Interpolation,Level set,Robustness (computer science),Image segmentation,Artificial intelligence,Time complexity,Flowchart | Conference |
ISSN | ISBN | Citations |
1945-7928 | 0-7803-9576-X | 1 |
PageRank | References | Authors |
0.35 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ek Tsoon Tan | 1 | 1 | 0.35 |
| Rajagopalan Srinivasan | 2 | 6 | 1.52 |
| Richard A. Robb | 3 | 645 | 238.12 |