The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition

In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to extract the required pathological features to identi...

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Autores principales:	Srinivasu Polinati, Durga Prasad Bavirisetti, Kandala N V P S Rajesh, Ganesh R Naik, Ravindra Dhuli
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	MRI CT Image fusion intrinsic mode functions (IMFs) LEM VMD Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/c7f5885c9d994053a577796067deaaf1
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spelling	oai:doaj.org-article:c7f5885c9d994053a577796067deaaf12021-11-25T16:42:14ZThe Fusion of MRI and CT Medical Images Using Variational Mode Decomposition10.3390/app1122109752076-3417https://doaj.org/article/c7f5885c9d994053a577796067deaaf12021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10975https://doaj.org/toc/2076-3417In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to extract the required pathological features to identify suspicious tissue details. Several medical image fusion methods have attempted to combine complementary information from MRI and CT to address the issue mentioned earlier over the past few decades. However, existing methods have their advantages and drawbacks. In this work, we propose a new multimodal medical image fusion approach based on variational mode decomposition (VMD) and local energy maxima (LEM). With the help of VMD, we decompose source images into several intrinsic mode functions (IMFs) to effectively extract edge details by avoiding boundary distortions. LEM is employed to carefully combine the IMFs based on the local information, which plays a crucial role in the fused image quality by preserving the appropriate spatial information. The proposed method’s performance is evaluated using various subjective and objective measures. The experimental analysis shows that the proposed method gives promising results compared to other existing and well-received fusion methods.Srinivasu PolinatiDurga Prasad BavirisettiKandala N V P S RajeshGanesh R NaikRavindra DhuliMDPI AGarticleMRICTImage fusionintrinsic mode functions (IMFs)LEMVMDTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10975, p 10975 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MRI CT Image fusion intrinsic mode functions (IMFs) LEM VMD Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	MRI CT Image fusion intrinsic mode functions (IMFs) LEM VMD Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Srinivasu Polinati Durga Prasad Bavirisetti Kandala N V P S Rajesh Ganesh R Naik Ravindra Dhuli The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
description	In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to extract the required pathological features to identify suspicious tissue details. Several medical image fusion methods have attempted to combine complementary information from MRI and CT to address the issue mentioned earlier over the past few decades. However, existing methods have their advantages and drawbacks. In this work, we propose a new multimodal medical image fusion approach based on variational mode decomposition (VMD) and local energy maxima (LEM). With the help of VMD, we decompose source images into several intrinsic mode functions (IMFs) to effectively extract edge details by avoiding boundary distortions. LEM is employed to carefully combine the IMFs based on the local information, which plays a crucial role in the fused image quality by preserving the appropriate spatial information. The proposed method’s performance is evaluated using various subjective and objective measures. The experimental analysis shows that the proposed method gives promising results compared to other existing and well-received fusion methods.
format	article
author	Srinivasu Polinati Durga Prasad Bavirisetti Kandala N V P S Rajesh Ganesh R Naik Ravindra Dhuli
author_facet	Srinivasu Polinati Durga Prasad Bavirisetti Kandala N V P S Rajesh Ganesh R Naik Ravindra Dhuli
author_sort	Srinivasu Polinati
title	The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
title_short	The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
title_full	The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
title_fullStr	The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
title_full_unstemmed	The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
title_sort	fusion of mri and ct medical images using variational mode decomposition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c7f5885c9d994053a577796067deaaf1
work_keys_str_mv	AT srinivasupolinati thefusionofmriandctmedicalimagesusingvariationalmodedecomposition AT durgaprasadbavirisetti thefusionofmriandctmedicalimagesusingvariationalmodedecomposition AT kandalanvpsrajesh thefusionofmriandctmedicalimagesusingvariationalmodedecomposition AT ganeshrnaik thefusionofmriandctmedicalimagesusingvariationalmodedecomposition AT ravindradhuli thefusionofmriandctmedicalimagesusingvariationalmodedecomposition AT srinivasupolinati fusionofmriandctmedicalimagesusingvariationalmodedecomposition AT durgaprasadbavirisetti fusionofmriandctmedicalimagesusingvariationalmodedecomposition AT kandalanvpsrajesh fusionofmriandctmedicalimagesusingvariationalmodedecomposition AT ganeshrnaik fusionofmriandctmedicalimagesusingvariationalmodedecomposition AT ravindradhuli fusionofmriandctmedicalimagesusingvariationalmodedecomposition
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The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition

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