An Object-Based Method for Rician Noise Estimation in MR Images

  • Authors:

  • Pierrick Coupé;José V. Manjón;Elias Gedamu;Douglas Arnold;Montserrat Robles;D. Louis Collins

  • Affiliations:

  • McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada H3A 2B4;Biomedical Informatics Group (IBIME), ITACA Institute, Universidad Politécnica de Valencia, Valencia, Spain 46022;McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada H3A 2B4;McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada H3A 2B4;Biomedical Informatics Group (IBIME), ITACA Institute, Universidad Politécnica de Valencia, Valencia, Spain 46022;McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada H3A 2B4

  • Venue:
  • MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
  • Year:
  • 2009

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Abstract

The estimation of the noise level in MR images is used to assess the consistency of statistical analysis or as an input parameter in some image processing techniques. Most of the existing Rician noise estimation methods are based on background statistics, and as such are sensitive to ghosting artifacts. In this paper, a new object-based method is proposed. This method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The adaptation for Rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the SNR of the image. A quantitative validation on synthetic phantom with artefacts is presented and a new validation framework is proposed to perform quantitative validation on real data. The results show the accuracy and the robustness of the proposed method.