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Realizing the Effective Detection of Tumor in Magnetic Resonance Imaging using Cluster-Sparse Assisted Super-Resolution



Kathiravan Srinivasan1, *, Ramaneswaran Selvakumar1, Sivakumar Rajagopal2, Dimiter Georgiev Velev3, Branislav Vuksanovic4
1 School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, India
2 School of Electronics Engineering, Vellore Institute of Technology (VIT), Vellore, India
3 Department of Information Technologies and Communications, University of National and World Economy, Sofia, Bulgaria
4 School of Engineering, University of Portsmouth, Portsmouth, England


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Creative Commons License
© 2021 Srinivasan et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, India; E-mail: kathiravan.srinivasan@vit.ac.in


Abstract

Recently, significant research has been done in Super-Resolution (SR) methods for augmenting the spatial resolution of the Magnetic Resonance (MR) images, which aids the physician in improved disease diagnoses. Single SR methods have drawbacks; they fail to capture self-similarity in non-local patches and are not robust to noise. To exploit the non-local self-similarity and intrinsic sparsity in MR images, this paper proposes the use of Cluster-Sparse Assisted Super-Resolution. This SR method effectively captures similarity in non-locally positioned patches by training on clusters of patches using a self-adaptive dictionary. This method of training also leads to better edge and texture detection. Experiments show that using Cluster-Sparse Assisted Super-Resolution for brain MR images results in enhanced detection of lesions leading to better diagnosis.

Keywords: Cluster-sparse assisted super-resolution, Magnetic resonance imaging, Tumor detection, Medical imaging, Positron emission tomography, Computed tomography.