Robust regression and outlier detection
Robust regression and outlier detection
An Extensible MRI Simulator for Post-Processing Evaluation
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Spatial decision forests for MS lesion segmentation in multi-channel MR images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
STREM: a robust multidimensional parametric method to segment MS lesions in MRI
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A novel fuzzy Dempster-Shafer inference system for brain MRI segmentation
Information Sciences: an International Journal
| Hi-index | 0.00 |
This article is about bias field correction in MR brain images. In the literature, most of the methods consist in modeling the imaging process before identifying its unknown parameters. After identifying two of the most widely used such models, we propose a third one and show that for these three models, it is possible to use a common estimation framework, based on the Maximum Likelihood principle. This scheme partly rests on a functional modeling of the bias field. The optimization is performed by an ECM algorithm, in which we have included a procedure of outliers rejection. In this way, we derive three algorithms and compare them on a set of simulated images. We also provide results on real MR images exhibiting a bias field with a typical "diagonal" pattern.