Denoising Multi-coil Magnetic Resonance Imaging Using Nonlocal Means on Extended LMMSE

  • V. Soumya
  • Abraham Varghese
  • T. Manesh
  • K. N. Neetha
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 425)

Abstract

Denoising plays key role in the field of medical images. Reliable estimation and noise removal is very important for accurate diagnosis of the disease. This should be done in such a way that original resolution is retained while maintaining the valuable features. Multi-coil Magnetic Resonance Image(MRI) trails nonstationary noise following Rician and Noncentral Chi(nc-\(\chi \)) distribution. On using the modern techniques which make use of multi-coil MRI like in GRAPPA would yield nc-\(\chi \) distributed data. There has been lots of research done on the Rician nature but only few for nc-\(\chi \) distribution. The proposed method uses Nonlocal Mean(NLM) on extended Linear Minimum Mean Square Error(ELMMSE) for denoising multi-coil MRI having nc-\(\chi \) distributed data. The performance of the nonlocal scheme on multi-coil MRI is evaluated based on PSNR, SSIM and MSE and the result indicates proposed scheme is better than the existing scheme including Non local Maximum Likelihood(NLML), adaptive NLML and ELMMSE.

Keywords

Magnetic Resonance Image Mean Square Error Denoising Method Rician Nature Coil Magnetic Resonance Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • V. Soumya
    • 1
  • Abraham Varghese
    • 1
  • T. Manesh
    • 2
  • K. N. Neetha
    • 1
  1. 1.Department of Computer Science and EngineeringAdi Shankara Institute of Engineering and TechnologyErnakulamIndia
  2. 2.Prince Sattam Bin Abdul Aziz UniversityAl-kharjSaudi Arabia

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