Chandler, A.G.* ; Netsch, T.* ; Cocosco, C.A.* ; Schnabel, J.A.* ; Hawkes, D.J.*
Slice-to-volume registration using mutual information between probabilistic image classifications.
In:. SPIE, 2004. 1120-1129 (Proc. SPIE ; 5370 II)
Intensity based registration algorithms have proved to be accurate and robust for 3D-3D registration tasks. However, these methods utilise the information content within an image, and therefore their performance is hindered for image data that is sparse. This is the case for the registration of a single image slice to a 3D image volume. There are some important applications that could benefit from improved slice-to-volume registration, for example, the planning of magnetic resonance (MR) scans or cardiac MR imaging, where images are acquired as stacks of single slices. We have developed and validated an information based slice-to-volume registration algorithm that uses vector valued probabilistic images of tissue classification that have been derived from the original intensity images. We believe that using such methods inherently incorporates into the registration framework more information about the images, especially in images containing severe partial volume artifacts. Initial experimental results indicate that the suggested method can achieve a more robust registration compared to standard intensity based methods for the rigid registration of a single thick brain MR slice, containing severe partial volume artifacts in the through-plane direction, to a complete 3D MR brain volume.
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Times Cited
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Publikationstyp
Artikel: Konferenzbeitrag
Dokumenttyp
Typ der Hochschulschrift
Herausgeber
Schlagwörter
C-means ; Fuzzy Classification ; Normalised Mutual Information ; Slice-to-volume Registration
Keywords plus
Sprache
englisch
Veröffentlichungsjahr
2004
Prepublished im Jahr
HGF-Berichtsjahr
2004
ISSN (print) / ISBN
0277-786X
e-ISSN
1996-756X
ISBN
Bandtitel
Konferenztitel
Konferzenzdatum
Konferenzort
Konferenzband
Quellenangaben
Band: 5370 II,
Heft: ,
Seiten: 1120-1129
Artikelnummer: ,
Supplement: ,
Reihe
Verlag
SPIE
Verlagsort
Tag d. mündl. Prüfung
0000-00-00
Betreuer
Gutachter
Prüfer
Topic
Hochschule
Hochschulort
Fakultät
Veröffentlichungsdatum
0000-00-00
Anmeldedatum
0000-00-00
Anmelder/Inhaber
weitere Inhaber
Anmeldeland
Priorität
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-507100-001
Förderungen
Copyright
Erfassungsdatum
2022-05-25