Realizing the effective detection of tumor in magnetic resonance imaging using cluster-sparse assisted super-resolution

Kathiravan Srinivasan*, Ramaneswaran Selvakumar, Sivakumar Rajagopal, Dimiter Georgiev Velev, Branislav Vuksanovic

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)170-179
Number of pages10
JournalOpen Biomedical Engineering Journal
Volume15
Publication statusPublished - 31 Dec 2021

Keywords

  • Cluster-sparse assisted super-resolution
  • Computed tomography
  • Magnetic resonance imaging
  • Medical imaging
  • Positron emission tomography
  • Tumor detection

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