An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy
Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nagaraja Kumar, N. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Multi-modal medical image fusion High-frequency sub-bands and low frequency sub-bands Weighted-fast discrete curvelet transform Optimized type-2 fuzzy entropy |
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| Anmerkung: |
© The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 25(2022), 1 vom: 30. Aug., Seite 96-117 |
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| Übergeordnetes Werk: |
volume:25 ; year:2022 ; number:1 ; day:30 ; month:08 ; pages:96-117 |
| Links: |
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| DOI / URN: |
10.1007/s40815-022-01379-9 |
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| Katalog-ID: |
SPR049192744 |
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| 520 | |a Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. | ||
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10.1007/s40815-022-01379-9 doi (DE-627)SPR049192744 (SPR)s40815-022-01379-9-e DE-627 ger DE-627 rakwb eng Nagaraja Kumar, N. verfasserin aut An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. Multi-modal medical image fusion (dpeaa)DE-He213 High-frequency sub-bands and low frequency sub-bands (dpeaa)DE-He213 Weighted-fast discrete curvelet transform (dpeaa)DE-He213 Optimized type-2 fuzzy entropy (dpeaa)DE-He213 Approach (dpeaa)DE-He213 Hybrid jaya with sun flower optimization (dpeaa)DE-He213 Structural similarity index measure (dpeaa)DE-He213 Jayachandra Prasad, T. aut Prasad, K. Satya aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2022), 1 vom: 30. Aug., Seite 96-117 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2022 number:1 day:30 month:08 pages:96-117 https://dx.doi.org/10.1007/s40815-022-01379-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 1 30 08 96-117 |
| spelling |
10.1007/s40815-022-01379-9 doi (DE-627)SPR049192744 (SPR)s40815-022-01379-9-e DE-627 ger DE-627 rakwb eng Nagaraja Kumar, N. verfasserin aut An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. Multi-modal medical image fusion (dpeaa)DE-He213 High-frequency sub-bands and low frequency sub-bands (dpeaa)DE-He213 Weighted-fast discrete curvelet transform (dpeaa)DE-He213 Optimized type-2 fuzzy entropy (dpeaa)DE-He213 Approach (dpeaa)DE-He213 Hybrid jaya with sun flower optimization (dpeaa)DE-He213 Structural similarity index measure (dpeaa)DE-He213 Jayachandra Prasad, T. aut Prasad, K. Satya aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2022), 1 vom: 30. Aug., Seite 96-117 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2022 number:1 day:30 month:08 pages:96-117 https://dx.doi.org/10.1007/s40815-022-01379-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 1 30 08 96-117 |
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10.1007/s40815-022-01379-9 doi (DE-627)SPR049192744 (SPR)s40815-022-01379-9-e DE-627 ger DE-627 rakwb eng Nagaraja Kumar, N. verfasserin aut An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. Multi-modal medical image fusion (dpeaa)DE-He213 High-frequency sub-bands and low frequency sub-bands (dpeaa)DE-He213 Weighted-fast discrete curvelet transform (dpeaa)DE-He213 Optimized type-2 fuzzy entropy (dpeaa)DE-He213 Approach (dpeaa)DE-He213 Hybrid jaya with sun flower optimization (dpeaa)DE-He213 Structural similarity index measure (dpeaa)DE-He213 Jayachandra Prasad, T. aut Prasad, K. Satya aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2022), 1 vom: 30. Aug., Seite 96-117 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2022 number:1 day:30 month:08 pages:96-117 https://dx.doi.org/10.1007/s40815-022-01379-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 1 30 08 96-117 |
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10.1007/s40815-022-01379-9 doi (DE-627)SPR049192744 (SPR)s40815-022-01379-9-e DE-627 ger DE-627 rakwb eng Nagaraja Kumar, N. verfasserin aut An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. Multi-modal medical image fusion (dpeaa)DE-He213 High-frequency sub-bands and low frequency sub-bands (dpeaa)DE-He213 Weighted-fast discrete curvelet transform (dpeaa)DE-He213 Optimized type-2 fuzzy entropy (dpeaa)DE-He213 Approach (dpeaa)DE-He213 Hybrid jaya with sun flower optimization (dpeaa)DE-He213 Structural similarity index measure (dpeaa)DE-He213 Jayachandra Prasad, T. aut Prasad, K. Satya aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2022), 1 vom: 30. Aug., Seite 96-117 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2022 number:1 day:30 month:08 pages:96-117 https://dx.doi.org/10.1007/s40815-022-01379-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 1 30 08 96-117 |
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10.1007/s40815-022-01379-9 doi (DE-627)SPR049192744 (SPR)s40815-022-01379-9-e DE-627 ger DE-627 rakwb eng Nagaraja Kumar, N. verfasserin aut An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. Multi-modal medical image fusion (dpeaa)DE-He213 High-frequency sub-bands and low frequency sub-bands (dpeaa)DE-He213 Weighted-fast discrete curvelet transform (dpeaa)DE-He213 Optimized type-2 fuzzy entropy (dpeaa)DE-He213 Approach (dpeaa)DE-He213 Hybrid jaya with sun flower optimization (dpeaa)DE-He213 Structural similarity index measure (dpeaa)DE-He213 Jayachandra Prasad, T. aut Prasad, K. Satya aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 25(2022), 1 vom: 30. Aug., Seite 96-117 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:25 year:2022 number:1 day:30 month:08 pages:96-117 https://dx.doi.org/10.1007/s40815-022-01379-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 1 30 08 96-117 |
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Nagaraja Kumar, N. |
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Nagaraja Kumar, N. misc Multi-modal medical image fusion misc High-frequency sub-bands and low frequency sub-bands misc Weighted-fast discrete curvelet transform misc Optimized type-2 fuzzy entropy misc Approach misc Hybrid jaya with sun flower optimization misc Structural similarity index measure An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy |
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An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy Multi-modal medical image fusion (dpeaa)DE-He213 High-frequency sub-bands and low frequency sub-bands (dpeaa)DE-He213 Weighted-fast discrete curvelet transform (dpeaa)DE-He213 Optimized type-2 fuzzy entropy (dpeaa)DE-He213 Approach (dpeaa)DE-He213 Hybrid jaya with sun flower optimization (dpeaa)DE-He213 Structural similarity index measure (dpeaa)DE-He213 |
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misc Multi-modal medical image fusion misc High-frequency sub-bands and low frequency sub-bands misc Weighted-fast discrete curvelet transform misc Optimized type-2 fuzzy entropy misc Approach misc Hybrid jaya with sun flower optimization misc Structural similarity index measure |
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misc Multi-modal medical image fusion misc High-frequency sub-bands and low frequency sub-bands misc Weighted-fast discrete curvelet transform misc Optimized type-2 fuzzy entropy misc Approach misc Hybrid jaya with sun flower optimization misc Structural similarity index measure |
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An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy |
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Nagaraja Kumar, N. Jayachandra Prasad, T. Prasad, K. Satya |
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intelligent multimodal medical image fusion model based on improved fast discrete curvelet transform and type-2 fuzzy entropy |
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An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy |
| abstract |
Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Multi-modal medical image fusion has emerged as a famous and efficient tool in medical applications. The major goal of this fusion is to fuse diverse multi-modal medical images attained from varied imaging modalities into a single fused image, which is broadly utilized by surgeons for the precise diagnosis and treatment of diseases. Multi-modal medical images generally consist of captured images with the specific organ of a patient. These images will indicate a modality, which will provide the observed organ in a different way that leads to dissimilar examinations of a specific incident like stroke. The detection with more suitable clinical decisions is taken by the accurate analysis of each modality. Multi-modal medical imaging is an efficient research area that incorporates the improvement of robust techniques, which can facilitate the information of image fusion attained with diverse sets of modalities. The major goal of this paper is to develop the multi-modal medical image fusion model using the new hybrid meta-heuristic approach. At first, the high-frequency sub-bands and low frequency sub-bands of the images that to be fused split by the weighted fast discrete curvelet transform (W-FDCuT). Once the sub-bands are split, the high-frequency sub-bands of the two images are fused by the optimized Type-2 fuzzy entropy. On the other hand, Averaging approach is used for performing the fusion of low frequency sub-bands. Finally, the inverse W-FDCuT is done for generating the final fused image. To improvise the performance of W-FDCuT and Type-2 fuzzy entropy, the hybrid meta-heuristic algorithm named hybrid jaya with sun flower optimization (HJ-SFO) is adopted, thus enhances the significant parameters by maximizing the structural similarity index measure (SSIM). The comparison over the conventional image fusion models proves the efficiency of the proposed model in terms of diverse analysis. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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An Intelligent Multimodal Medical Image Fusion Model Based on Improved Fast Discrete Curvelet Transform and Type-2 Fuzzy Entropy |
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https://dx.doi.org/10.1007/s40815-022-01379-9 |
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Jayachandra Prasad, T. Prasad, K. Satya |
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10.1007/s40815-022-01379-9 |
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2024-07-03T23:45:54.226Z |
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| score |
7.4017887 |