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Heinrich, M.P.* ; Jenkinson, M.* ; Papiez, B.W.* ; Brady, S.M.* ; Schnabel, J.A.*

Towards realtime multimodal fusion for image-guided interventions using self-similarities.

In: (International Conference on Medical Image Computing and Computer-Assisted Intervention). Berlin [u.a.]: Springer, 2013. 187-194 (Lect. Notes Comput. Sc. ; 8149 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Image-guided interventions often rely on deformable multi-modal registration to align pre-treatment and intra-operative scans. There are a number of requirements for automated image registration for this task, such as a robust similarity metric for scans of different modalities with different noise distributions and contrast, an efficient optimisation of the cost function to enable fast registration for this time-sensitive application, and an insensitive choice of registration parameters to avoid delays in practical clinical use. In this work, we build upon the concept of structural image representation for multi-modal similarity. Discriminative descriptors are densely extracted for the multi-modal scans based on the "self-similarity context". An efficient quantised representation is derived that enables very fast computation of point-wise distances between descriptors. A symmetric multi-scale discrete optimisation with diffusion regularisation is used to find smooth transformations. The method is evaluated for the registration of 3D ultrasound and MRI brain scans for neurosurgery and demonstrates a significantly reduced registration error (on average 2.1 mm) compared to commonly used similarity metrics and computation times of less than 30 seconds per 3D registration. © 2013 Springer-Verlag.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Discrete Optimisation ; Multimodal Similarity ; Neurosurgery
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Conference on Medical Image Computing and Computer-Assisted Intervention
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 8149 LNCS, Heft: PART 1, Seiten: 187-194 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)