A multicomponent approach to nonrigid registration of diffusion tensor images
Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation...
Ausführliche Beschreibung
Autor*in: |
Khader, Mohammed [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Schlagwörter: |
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Anmerkung: |
© Springer Science+Business Media New York 2016 |
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Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 46(2016), 2 vom: 18. Aug., Seite 241-253 |
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Übergeordnetes Werk: |
volume:46 ; year:2016 ; number:2 ; day:18 ; month:08 ; pages:241-253 |
Links: |
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DOI / URN: |
10.1007/s10489-016-0833-8 |
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OLC2066102385 |
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700 | 1 | |a Hamza, A. Ben |4 aut | |
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10.1007/s10489-016-0833-8 doi (DE-627)OLC2066102385 (DE-He213)s10489-016-0833-8-p DE-627 ger DE-627 rakwb eng 004 VZ Khader, Mohammed verfasserin aut A multicomponent approach to nonrigid registration of diffusion tensor images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion. Diffusion tensor imaging Image registration Nonrigid Tsallis entropy Schiavi, Emanuele aut Hamza, A. Ben aut Enthalten in Applied intelligence Springer US, 1991 46(2016), 2 vom: 18. Aug., Seite 241-253 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:46 year:2016 number:2 day:18 month:08 pages:241-253 https://doi.org/10.1007/s10489-016-0833-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 46 2016 2 18 08 241-253 |
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10.1007/s10489-016-0833-8 doi (DE-627)OLC2066102385 (DE-He213)s10489-016-0833-8-p DE-627 ger DE-627 rakwb eng 004 VZ Khader, Mohammed verfasserin aut A multicomponent approach to nonrigid registration of diffusion tensor images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion. Diffusion tensor imaging Image registration Nonrigid Tsallis entropy Schiavi, Emanuele aut Hamza, A. Ben aut Enthalten in Applied intelligence Springer US, 1991 46(2016), 2 vom: 18. Aug., Seite 241-253 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:46 year:2016 number:2 day:18 month:08 pages:241-253 https://doi.org/10.1007/s10489-016-0833-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 46 2016 2 18 08 241-253 |
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10.1007/s10489-016-0833-8 doi (DE-627)OLC2066102385 (DE-He213)s10489-016-0833-8-p DE-627 ger DE-627 rakwb eng 004 VZ Khader, Mohammed verfasserin aut A multicomponent approach to nonrigid registration of diffusion tensor images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion. Diffusion tensor imaging Image registration Nonrigid Tsallis entropy Schiavi, Emanuele aut Hamza, A. Ben aut Enthalten in Applied intelligence Springer US, 1991 46(2016), 2 vom: 18. Aug., Seite 241-253 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:46 year:2016 number:2 day:18 month:08 pages:241-253 https://doi.org/10.1007/s10489-016-0833-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 46 2016 2 18 08 241-253 |
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10.1007/s10489-016-0833-8 doi (DE-627)OLC2066102385 (DE-He213)s10489-016-0833-8-p DE-627 ger DE-627 rakwb eng 004 VZ Khader, Mohammed verfasserin aut A multicomponent approach to nonrigid registration of diffusion tensor images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion. Diffusion tensor imaging Image registration Nonrigid Tsallis entropy Schiavi, Emanuele aut Hamza, A. Ben aut Enthalten in Applied intelligence Springer US, 1991 46(2016), 2 vom: 18. Aug., Seite 241-253 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:46 year:2016 number:2 day:18 month:08 pages:241-253 https://doi.org/10.1007/s10489-016-0833-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 46 2016 2 18 08 241-253 |
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Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion. © Springer Science+Business Media New York 2016 |
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Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion. © Springer Science+Business Media New York 2016 |
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Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion. © Springer Science+Business Media New York 2016 |
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