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Multimodal brain image fusion based on error texture elimination and salient feature detection
As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base l...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372795/ https://www.ncbi.nlm.nih.gov/pubmed/37521686 http://dx.doi.org/10.3389/fnins.2023.1204263 |
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author | Li, Xilai Li, Xiaosong |
author_facet | Li, Xilai Li, Xiaosong |
author_sort | Li, Xilai |
collection | PubMed |
description | As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base layers, ignoring the fact that useful texture information may exist in pixels with lower energy values. Thus, erroneous textures may be introduced into the fusion results. To resolve this problem, we propose a novel multimodal brain image fusion algorithm based on error texture removal. A two-layer decomposition scheme is first implemented to generate the high- and low-frequency subbands. We propose a salient feature detection operator based on gradient difference and entropy. The proposed operator integrates the gradient difference and amount of information in the high-frequency subbands to effectively identify clearly detailed information. Subsequently, we detect the energy information of the low-frequency subband by utilizing the local phase feature of each pixel as the intensity measurement and using a random walk algorithm to detect the energy information. Finally, we propose a rolling guidance filtering iterative least-squares model to reconstruct the texture information in the low-frequency components. Through extensive experiments, we successfully demonstrate that the proposed algorithm outperforms some state-of-the-art methods. Our source code is publicly available at https://github.com/ixilai/ETEM. |
format | Online Article Text |
id | pubmed-10372795 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103727952023-07-28 Multimodal brain image fusion based on error texture elimination and salient feature detection Li, Xilai Li, Xiaosong Front Neurosci Neuroscience As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base layers, ignoring the fact that useful texture information may exist in pixels with lower energy values. Thus, erroneous textures may be introduced into the fusion results. To resolve this problem, we propose a novel multimodal brain image fusion algorithm based on error texture removal. A two-layer decomposition scheme is first implemented to generate the high- and low-frequency subbands. We propose a salient feature detection operator based on gradient difference and entropy. The proposed operator integrates the gradient difference and amount of information in the high-frequency subbands to effectively identify clearly detailed information. Subsequently, we detect the energy information of the low-frequency subband by utilizing the local phase feature of each pixel as the intensity measurement and using a random walk algorithm to detect the energy information. Finally, we propose a rolling guidance filtering iterative least-squares model to reconstruct the texture information in the low-frequency components. Through extensive experiments, we successfully demonstrate that the proposed algorithm outperforms some state-of-the-art methods. Our source code is publicly available at https://github.com/ixilai/ETEM. Frontiers Media S.A. 2023-07-13 /pmc/articles/PMC10372795/ /pubmed/37521686 http://dx.doi.org/10.3389/fnins.2023.1204263 Text en Copyright © 2023 Li and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Li, Xilai Li, Xiaosong Multimodal brain image fusion based on error texture elimination and salient feature detection |
title | Multimodal brain image fusion based on error texture elimination and salient feature detection |
title_full | Multimodal brain image fusion based on error texture elimination and salient feature detection |
title_fullStr | Multimodal brain image fusion based on error texture elimination and salient feature detection |
title_full_unstemmed | Multimodal brain image fusion based on error texture elimination and salient feature detection |
title_short | Multimodal brain image fusion based on error texture elimination and salient feature detection |
title_sort | multimodal brain image fusion based on error texture elimination and salient feature detection |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372795/ https://www.ncbi.nlm.nih.gov/pubmed/37521686 http://dx.doi.org/10.3389/fnins.2023.1204263 |
work_keys_str_mv | AT lixilai multimodalbrainimagefusionbasedonerrortextureeliminationandsalientfeaturedetection AT lixiaosong multimodalbrainimagefusionbasedonerrortextureeliminationandsalientfeaturedetection |