Name
Abstract | ||
|---|---|---|
This work presents a new segmentation model called Similarity Cloud Model (SCM) based on hippocampus feature extraction. The segmentation process is divided in two main operations: localization by similarity and cloud adjustment. The first process uses the cloud to localize the most probable position of the hippocampus in a target volume. Segmentation is completed by a reformulation of the cloud to correct the final labeling, based on a new computation of arc-weights. This method has been tested in an entire dataset of 235 MRI combining healthy and epileptic patients. Results indicate superior quality segmentation in comparison with similar graph and bayesian-based models. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CBMS.2011.5999148 | CBMS |
Keywords | Field | DocType |
efficient hippocampus segmentation technique,similarity cloud model,hippocampus feature extraction,new segmentation model,entire dataset,superior quality segmentation,new computation,segmentation process,epileptic patient,bayesian-based model,cloud adjustment,localization,estimation,computational modeling,computer model,feature extraction,mri,hippocampus,image segmentation,shape,uncertainty | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Feature extraction,Image segmentation,Artificial intelligence,Computation,Cloud computing,Bayesian probability | Conference |
ISSN | ISBN | Citations |
2372-9198 | 978-1-4577-1189-3 | 1 |
PageRank | References | Authors |
0.35 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. E. C. Atho | 1 | 1 | 0.35 |
| A. J. M. Traina | 2 | 119 | 6.41 |
| C. Traina | 3 | 2 | 0.69 |
| Paula Diniz | 4 | 1 | 2.37 |
| Antonio C. dos Santos | 5 | 1 | 0.35 |