Cargando…
A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique
Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach th...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376225/ https://www.ncbi.nlm.nih.gov/pubmed/37508846 http://dx.doi.org/10.3390/bioengineering10070819 |
_version_ | 1785079218210603008 |
---|---|
author | Darwish, Saad M. Abu Shaheen, Lina J. Elzoghabi, Adel A. |
author_facet | Darwish, Saad M. Abu Shaheen, Lina J. Elzoghabi, Adel A. |
author_sort | Darwish, Saad M. |
collection | PubMed |
description | Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm’s mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS’ 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results. |
format | Online Article Text |
id | pubmed-10376225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103762252023-07-29 A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique Darwish, Saad M. Abu Shaheen, Lina J. Elzoghabi, Adel A. Bioengineering (Basel) Article Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm’s mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS’ 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results. MDPI 2023-07-09 /pmc/articles/PMC10376225/ /pubmed/37508846 http://dx.doi.org/10.3390/bioengineering10070819 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Article Darwish, Saad M. Abu Shaheen, Lina J. Elzoghabi, Adel A. A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique |
title | A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique |
title_full | A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique |
title_fullStr | A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique |
title_full_unstemmed | A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique |
title_short | A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique |
title_sort | new medical analytical framework for automated detection of mri brain tumor using evolutionary quantum inspired level set technique |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376225/ https://www.ncbi.nlm.nih.gov/pubmed/37508846 http://dx.doi.org/10.3390/bioengineering10070819 |
work_keys_str_mv | AT darwishsaadm anewmedicalanalyticalframeworkforautomateddetectionofmribraintumorusingevolutionaryquantuminspiredlevelsettechnique AT abushaheenlinaj anewmedicalanalyticalframeworkforautomateddetectionofmribraintumorusingevolutionaryquantuminspiredlevelsettechnique AT elzoghabiadela anewmedicalanalyticalframeworkforautomateddetectionofmribraintumorusingevolutionaryquantuminspiredlevelsettechnique AT darwishsaadm newmedicalanalyticalframeworkforautomateddetectionofmribraintumorusingevolutionaryquantuminspiredlevelsettechnique AT abushaheenlinaj newmedicalanalyticalframeworkforautomateddetectionofmribraintumorusingevolutionaryquantuminspiredlevelsettechnique AT elzoghabiadela newmedicalanalyticalframeworkforautomateddetectionofmribraintumorusingevolutionaryquantuminspiredlevelsettechnique |