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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information.

Details

Title
Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform
Author
Diwakar, Manoj 1   VIAFID ORCID Logo  ; Singh, Prabhishek 2   VIAFID ORCID Logo  ; Singh, Ravinder 3 ; Sisodia, Dilip 3   VIAFID ORCID Logo  ; Singh, Vijendra 4   VIAFID ORCID Logo  ; Maurya, Ankur 2 ; Kadry, Seifedine 5   VIAFID ORCID Logo  ; Sevcik, Lukas 6   VIAFID ORCID Logo 

 Department of Computer Science and Engineering, Graphic Era (Deemed to Be University), Dehradun 248002, Uttarakhand, India 
 School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, Uttar Pradesh, India 
 Department of Computer Science and Engineering, Engineering College Ajmer, Ajmer 305025, Rajasthan, India 
 School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India 
 Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon 
 University of Zilina, Univerzitna 1, 01026 Zilina, Slovakia 
First page
1395
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20754418
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2806519891
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.