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Denoising of Volumetric MR Image Using Low-Rank Approximation on Tensor SVD Framework

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Proceedings of 2nd International Conference on Computer Vision & Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 703))

Abstract

In this paper, we focus on denoising of additively corrupted volumetric magnetic resonance (MR) images for improved clinical diagnosis and further processing. We have considered three dimensional MR images as third-order tensors. MR image denoising is solved as a low-rank tensor approximation problem, where the non-local similarity and correlation existing in volumetric MR images are exploited. The corrupted images are divided into 3D patches and similar patches form a group matrix. The group matrices exhibit low-rank property and is decomposed with tensor singular value decomposition (t-SVD) technique, and reweighted iterative thresholding is performed on core coefficients for removing the noise. The proposed method is compared with the state-of-the-art methods and has shown improved performance.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, 673601, India

    Hawazin S. Khaleel, Sameera V. Mohd Sagheer, M. Baburaj & Sudhish N. George

Authors
  1. Hawazin S. Khaleel
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  2. Sameera V. Mohd Sagheer
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  3. M. Baburaj
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  4. Sudhish N. George
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Corresponding author

Correspondence to Hawazin S. Khaleel .

Editor information

Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Mohan S. Kankanhalli

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Khaleel, H.S., Mohd Sagheer, S.V., Baburaj, M., George, S.N. (2018). Denoising of Volumetric MR Image Using Low-Rank Approximation on Tensor SVD Framework. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-10-7895-8_29

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  • DOI: https://doi.org/10.1007/978-981-10-7895-8_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7894-1

  • Online ISBN: 978-981-10-7895-8

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