REVIEW ARTICLE
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
Article Information
Identifiers and Pagination:
Year: 2021Volume: 15
Issue: Suppl-2, M6
First Page: 170
Last Page: 179
Publisher ID: TOBEJ-15-170
DOI: 10.2174/1874120702115010170
Article History:
Received Date: 07/9/2020Revision Received Date: 2/4/2021
Acceptance Date: 5/4/2021
Electronic publication date: 31/12/2021
Collection year: 2021
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.
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.