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New Tools for MRI processing: Segmentation, Denoising, Super-resolution (Pierrick Coupe's Open Source Contributions)

Computer Vision Central - Posted on December 25, 2011 at 1:08 pm.

  • Links: https://sites.google.com/site/pierrickcoupe/softwares
  • Details:
  • Pierrick Coupe's Open Source Contributions: Based on Maltab C-Mex code, the proposed tools derives from my recent research on nonlocal means estimator.


    In 3D Magnetic Resonance Imaging (MRI), image resolution is limited by several factors such as hardware, time constraints or patient’s comfort. Therefore, the low resolution of MRI can limit accuracy of post-processing tasks such as lesion segmentation (e.g., brain tumor or multiple sclerosis). In order to efficiently reconstruct high resolution MRI from low resolution MRI a new patch-based method has been proposed to recover high frequency information by using a data-adaptive reconstruction in combination with a sub-sampling coherence constraint. The proposed method has been applied in mono and multi-modal context.

    Monomodal MRI super-resolution

    Noise estimation

    Robust rician noise estimation for 3D MRI 

    In this paper, a new object-based method to estimate noise in magnitude MR images is proposed. The main advantage of this object-based method is its robustness to background artefacts such as ghosting. The proposed method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The MAD is a robust and efficient estimator initially proposed to estimate Gaussian noise. In this work, the adaptation of MAD operator 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. During the evaluation, a comparison of the proposed method with several state-of-the-art methods is performed. A quantitative validation on synthetic phantom with and without artefacts is presented. A new validation framework is proposed to perform quantitative validation on real data. The impact of the accuracy of noise estimation on the performance of a denoising filter is also studied. The results obtained on synthetic images show the accuracy and the robustness of the proposed method. Within the validation on real data, the proposed method obtained very competitive results compared to the methods under study.
    • Matlab code : 
    • C++ implementation as MINC tool : 

    Reference: P. Coupé, J. V. Manjon, E. Gedamu, D. Arnold, M. Robles, D. L. Collins. Robust Rician Noise Estimation for MR Images. Medical Image Analysis, 14(4) : 483–493, 2010.   


    Denoising is a crucial step used to increase image quality and to improve performance of all the tasks needed for quantitative imaging analysis. The main challenge is to remove noise component while keeping the integrity of relevant image information. This later aspect is important especially in context of medical image processing. To address this issue, the well-known Non-local Means filter (NLM) has been adapted to medical image analysis. First, the NLM filter has been extended to 3D Magnetic Resonance images (MRI). MRI specificities have been taken into account such as Rician noise, non-stationary noise and image dimensionality. Second, a Bayesian definition of the NLM filter has been proposed in order to adapt the NLM filter to ultrasound imaging. Finally, a new collaborative framework has been proposed to multi-photon image filtering.

    Magnetic Resonance Image Denoising

    Speckle Reduction for Ultrasound Imaging

    Multi-photon Microscopy Filtering


    Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of brain structures. Volume and shape of these anatomical structures are useful for pathology detection and population comparison. However, the high inter-subject variability and the modifications caused by pathology make automatic segmentation very challenging. A novel patch-based method using expert manual segmentations as priors has been proposed to achieve this task. Inspired by recent works in image denoising and label fusion segmentation, this new method has been adapted to segmentation of complex structures such as hippocampus and to brain extraction.

    Anatomical Brain Structure Segmentation

    Brain Extraction

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