Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain
Abstract Multimodal medical image is an effective method to solve a series of clinical problems, such as clinical diagnosis and postoperative treatment. In this study, a medical image fusion method based on convolutional sparse representation (CSR) and mutual information correlation is proposed. In...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-022-00792-9 |
| _version_ | 1797863156700151808 |
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| author | Peng Guo Guoqi Xie Renfa Li Hui Hu |
| author_facet | Peng Guo Guoqi Xie Renfa Li Hui Hu |
| author_sort | Peng Guo |
| collection | DOAJ |
| description | Abstract Multimodal medical image is an effective method to solve a series of clinical problems, such as clinical diagnosis and postoperative treatment. In this study, a medical image fusion method based on convolutional sparse representation (CSR) and mutual information correlation is proposed. In this method, the source image is decomposed into one high-frequency and one low-frequency sub-band by non-subsampled shearlet transform. For the high-frequency sub-band, CSR is used for high-frequency coefficient fusion. For the low-frequency sub-band, different fusion strategies are used for different regions by mutual information correlation analysis. Analysis of two kinds of medical image fusion problems, namely, CT–MRI and MRI–SPECT, reveals that the performance of this method is robust in terms of five common objective metrics. Compared with the other six advanced medical image fusion methods, the experimental results show that the proposed method achieves better results in subjective vision and objective evaluation metrics. |
| first_indexed | 2024-04-09T22:32:07Z |
| format | Article |
| id | doaj.art-cac38ec81faf46df8c9516a47489efda |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-09T22:32:07Z |
| publishDate | 2022-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-cac38ec81faf46df8c9516a47489efda2023-03-22T12:44:09ZengSpringerComplex & Intelligent Systems2199-45362198-60532022-06-019131732810.1007/s40747-022-00792-9Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domainPeng Guo0Guoqi Xie1Renfa Li2Hui Hu3School of Computer and Communication, Hunan Institute of EngineeringCollege of Computer Science and Electronic Engineering, Hunan UniversityCollege of Computer Science and Electronic Engineering, Hunan UniversitySchool of Computer and Communication, Hunan Institute of EngineeringAbstract Multimodal medical image is an effective method to solve a series of clinical problems, such as clinical diagnosis and postoperative treatment. In this study, a medical image fusion method based on convolutional sparse representation (CSR) and mutual information correlation is proposed. In this method, the source image is decomposed into one high-frequency and one low-frequency sub-band by non-subsampled shearlet transform. For the high-frequency sub-band, CSR is used for high-frequency coefficient fusion. For the low-frequency sub-band, different fusion strategies are used for different regions by mutual information correlation analysis. Analysis of two kinds of medical image fusion problems, namely, CT–MRI and MRI–SPECT, reveals that the performance of this method is robust in terms of five common objective metrics. Compared with the other six advanced medical image fusion methods, the experimental results show that the proposed method achieves better results in subjective vision and objective evaluation metrics.https://doi.org/10.1007/s40747-022-00792-9Medical image fusionNSSTConvolution sparse representationMutual information correlation |
| spellingShingle | Peng Guo Guoqi Xie Renfa Li Hui Hu Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain Complex & Intelligent Systems Medical image fusion NSST Convolution sparse representation Mutual information correlation |
| title | Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain |
| title_full | Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain |
| title_fullStr | Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain |
| title_full_unstemmed | Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain |
| title_short | Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain |
| title_sort | multimodal medical image fusion with convolution sparse representation and mutual information correlation in nsst domain |
| topic | Medical image fusion NSST Convolution sparse representation Mutual information correlation |
| url | https://doi.org/10.1007/s40747-022-00792-9 |
| work_keys_str_mv | AT pengguo multimodalmedicalimagefusionwithconvolutionsparserepresentationandmutualinformationcorrelationinnsstdomain AT guoqixie multimodalmedicalimagefusionwithconvolutionsparserepresentationandmutualinformationcorrelationinnsstdomain AT renfali multimodalmedicalimagefusionwithconvolutionsparserepresentationandmutualinformationcorrelationinnsstdomain AT huihu multimodalmedicalimagefusionwithconvolutionsparserepresentationandmutualinformationcorrelationinnsstdomain |