Realizing the Effective Detection of Tumor in Magnetic Resonance Imaging using Cluster-Sparse Assisted Super-Resolution release_v7gh4svtkzdbrfkslqobt5aeqy

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

Published in Open Biomedical Engineering Journal by Bentham Science Publishers Ltd..

2021   Volume 15, p170-179

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.
In application/xml+jats format

Archived Files and Locations

application/pdf  6.3 MB
file_qo7nqdkrqjgqxpognlmwgd54hq
openbiomedicalengineeringjournal.com (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-12-31
Language   en ?
Container Metadata
Not in DOAJ
In Keepers Registry
ISSN-L:  1874-1207
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: aa2df68f-7bc5-4184-8dfb-935d15120105
API URL: JSON