Anatomical-functional image fusion by information of interest in local Laplacian filtering domain
A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating le...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Du, Jiao [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
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Enthalten in: IEEE transactions on image processing - New York, NY : Inst., 1992, PP, 99, Seite 1-1 |
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| Übergeordnetes Werk: |
volume:PP ; number:99 ; pages:1-1 |
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| DOI / URN: |
10.1109/TIP.2017.2745202 |
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| 520 | |a A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods. | ||
| 650 | 4 | |a Transforms | |
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| 700 | 1 | |a Li, Weisheng |4 oth | |
| 700 | 1 | |a Xiao, Bin |4 oth | |
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10.1109/TIP.2017.2745202 doi PQ20171228 (DE-627)OLC1999535278 (DE-599)GBVOLC1999535278 (PRQ)i948-f9f2795b62c4a9c32121968e54b30d904b4548b2dbbe700edac83f5ae40af700 (KEY)0213811500000000000009900001anatomicalfunctionalimagefusionbyinformationofinte DE-627 ger DE-627 rakwb eng 004 620 DE-600 54.00 bkl Du, Jiao verfasserin aut Anatomical-functional image fusion by information of interest in local Laplacian filtering domain Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods. Transforms Biomedical imaging Filtering Laplace equations Image edge detection Image fusion Image decomposition Li, Weisheng oth Xiao, Bin oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 PP, 99, Seite 1-1 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/TIP.2017.2745202 Volltext http://ieeexplore.ieee.org/document/8016654 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_20 54.00 AVZ AR PP 99 1-1 |
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10.1109/TIP.2017.2745202 doi PQ20171228 (DE-627)OLC1999535278 (DE-599)GBVOLC1999535278 (PRQ)i948-f9f2795b62c4a9c32121968e54b30d904b4548b2dbbe700edac83f5ae40af700 (KEY)0213811500000000000009900001anatomicalfunctionalimagefusionbyinformationofinte DE-627 ger DE-627 rakwb eng 004 620 DE-600 54.00 bkl Du, Jiao verfasserin aut Anatomical-functional image fusion by information of interest in local Laplacian filtering domain Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods. Transforms Biomedical imaging Filtering Laplace equations Image edge detection Image fusion Image decomposition Li, Weisheng oth Xiao, Bin oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 PP, 99, Seite 1-1 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/TIP.2017.2745202 Volltext http://ieeexplore.ieee.org/document/8016654 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_20 54.00 AVZ AR PP 99 1-1 |
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10.1109/TIP.2017.2745202 doi PQ20171228 (DE-627)OLC1999535278 (DE-599)GBVOLC1999535278 (PRQ)i948-f9f2795b62c4a9c32121968e54b30d904b4548b2dbbe700edac83f5ae40af700 (KEY)0213811500000000000009900001anatomicalfunctionalimagefusionbyinformationofinte DE-627 ger DE-627 rakwb eng 004 620 DE-600 54.00 bkl Du, Jiao verfasserin aut Anatomical-functional image fusion by information of interest in local Laplacian filtering domain Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods. Transforms Biomedical imaging Filtering Laplace equations Image edge detection Image fusion Image decomposition Li, Weisheng oth Xiao, Bin oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 PP, 99, Seite 1-1 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/TIP.2017.2745202 Volltext http://ieeexplore.ieee.org/document/8016654 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_20 54.00 AVZ AR PP 99 1-1 |
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10.1109/TIP.2017.2745202 doi PQ20171228 (DE-627)OLC1999535278 (DE-599)GBVOLC1999535278 (PRQ)i948-f9f2795b62c4a9c32121968e54b30d904b4548b2dbbe700edac83f5ae40af700 (KEY)0213811500000000000009900001anatomicalfunctionalimagefusionbyinformationofinte DE-627 ger DE-627 rakwb eng 004 620 DE-600 54.00 bkl Du, Jiao verfasserin aut Anatomical-functional image fusion by information of interest in local Laplacian filtering domain Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods. Transforms Biomedical imaging Filtering Laplace equations Image edge detection Image fusion Image decomposition Li, Weisheng oth Xiao, Bin oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 PP, 99, Seite 1-1 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/TIP.2017.2745202 Volltext http://ieeexplore.ieee.org/document/8016654 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_20 54.00 AVZ AR PP 99 1-1 |
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Anatomical-functional image fusion by information of interest in local Laplacian filtering domain |
| abstract |
A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods. |
| abstractGer |
A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods. |
| abstract_unstemmed |
A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_20 |
| container_issue |
99 |
| title_short |
Anatomical-functional image fusion by information of interest in local Laplacian filtering domain |
| url |
http://dx.doi.org/10.1109/TIP.2017.2745202 http://ieeexplore.ieee.org/document/8016654 |
| remote_bool |
false |
| author2 |
Li, Weisheng Xiao, Bin |
| author2Str |
Li, Weisheng Xiao, Bin |
| ppnlink |
131074458 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1109/TIP.2017.2745202 |
| up_date |
2024-07-03T14:33:22.864Z |
| _version_ |
1803568758526050306 |
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|
| score |
7.400075 |