Name
Abstract | ||
|---|---|---|
Quantitative magnetic resonance analysis often requires accurate, robust and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert segmentation priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. During our experiments, the hippocampi of 80 healthy subjects were segmented. The influence on segmentation accuracy of different parameters such as patch size or number of training subjects was also studied. Moreover, a comparison with an appearance-based method and a template-based method was carried out. The highest median kappa value obtained with the proposed method was 0.884, which is competitive compared with recently published methods. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15711-0_17 | MICCAI (3) |
Keywords | Field | DocType |
high accuracy,segmentation accuracy,expert segmentation prior,robust segmentation,appearance-based method,novel patch-based method,label fusion strategy,hippocampus segmentation,nonlocal patch-based label fusion,template-based method,template-warping method,magnetic resonance | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Fusion,Artificial intelligence,Image denoising,Anatomical structures,Prior probability | Conference |
Volume | Issue | ISSN |
13 | Pt 3 | 0302-9743 |
ISBN | Citations | PageRank |
3-642-15710-6 | 19 | 1.20 |
References | Authors | |
14 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierrick Coupé | 1 | 1209 | 60.13 |
| José V. Manjón | 2 | 795 | 39.24 |
| Vladimir Fonov | 3 | 478 | 20.32 |
| Jens Pruessner | 4 | 60 | 3.86 |
| Montserrat Robles | 5 | 1064 | 58.83 |
| D. Louis Collins | 6 | 3915 | 403.90 |