Name
Abstract | ||
|---|---|---|
In this paper, we present a robust method to estimate parameters of hidden Markov chains (HMC) in order to segment brain MR images. Indeed, parameter estimation can be very sensitive to the presence of outliers in the data. We propose to use the trimmed likelihood estimator (TLE) to extract such outliers and to accurately estimate the parameters of different tissue classes in a robust way. Moreover neighborhood information is included in the model by using hidden Markov chains. Experimental results on 2D synthetic data and on 3D brain MRI are included to validate this approach. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICASSP.2008.4517660 | Las Vegas, NV |
Keywords | Field | DocType |
biomedical MRI,brain,hidden Markov models,image segmentation,maximum likelihood estimation,medical image processing,2D synthetic data,3D brain MRI segmentation,hidden Markov chain,image segmentation,neighborhood information,parameter estimation,robust method,tissue classification,trimmed likelihood estimator,Hidden Markov models,Image segmentation,Magnetic Resonance Imaging,robustness | Pattern recognition,Segmentation,Computer science,Outlier,Image segmentation,Robustness (computer science),Synthetic data,Artificial intelligence,Estimation theory,Hidden Markov model,Estimator | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4244-1484-0 | 978-1-4244-1484-0 | 1 |
PageRank | References | Authors |
0.37 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephanie Bricq | 1 | 51 | 3.82 |
| Christophe Collet | 2 | 246 | 35.46 |
| Jean-paul Armspach | 3 | 81 | 5.20 |