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
Gespeichert in:
| 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 |
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| DOI / URN: |
10.1007/s10489-016-0833-8 |
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OLC2066102385 |
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| 520 | |a 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. | ||
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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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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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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2066102385</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502204952.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-016-0833-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066102385</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-016-0833-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Khader, Mohammed</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A multicomponent approach to nonrigid registration of diffusion tensor images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. 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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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diffusion tensor imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image registration</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonrigid</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tsallis entropy</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schiavi, Emanuele</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hamza, A. Ben</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">46(2016), 2 vom: 18. Aug., Seite 241-253</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:46</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:2</subfield><subfield code="g">day:18</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:241-253</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-016-0833-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">46</subfield><subfield code="j">2016</subfield><subfield code="e">2</subfield><subfield code="b">18</subfield><subfield code="c">08</subfield><subfield code="h">241-253</subfield></datafield></record></collection>
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