Trimmed-Likelihood Estimation for Focal Lesions and Tissue Segmentation in Multisequence MRI for Multiple Sclerosis

@article{GarcaLorenzo2011TrimmedLikelihoodEF,
  title={Trimmed-Likelihood Estimation for Focal Lesions and Tissue Segmentation in Multisequence MRI for Multiple Sclerosis},
  author={Daniel Garc{\'i}a-Lorenzo and Sylvain Prima and Douglas L. Arnold and D. Louis Collins and Christian Barillot},
  journal={IEEE Transactions on Medical Imaging},
  year={2011},
  volume={30},
  pages={1455-1467}
}
We present a new automatic method for segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. The method performs tissue classification using a model of intensities of the normal appearing brain tissues. In order to estimate the model, a trimmed likelihood estimator is initialized with a hierarchical random approach in order to be robust to MS lesions and other outliers present in real images. The algorithm is first evaluated with simulated images to assess the importance… CONTINUE READING