Fast registration of 3D fetal ultrasound images using learned corresponding salient points
Gomez A, Bhatia K, Tharin S, Housden J, Toussaint N, Schnabel JA (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: Springer Verlag
Book Volume: 10554 LNCS
Pages Range: 33-41
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Quebec City, QC, CAN
ISBN: 9783319675602
DOI: 10.1007/978-3-319-67561-9_4
Abstract
We propose a fast feature-based rigid registration framework with a novel feature saliency detection technique. The method works by automatically classifying candidate image points as salient or non-salient using a support vector machine trained on points which have previously driven successful registrations. Resulting candidate salient points are used for symmetric matching based on local descriptor similarity and followed by RANSAC outlier rejection to obtain the final transform. The proposed registration framework was applied to 3D real-time fetal ultrasound images, thus covering the entire fetal anatomy for extended FoV imaging. Our method was applied to data from 5 patients, and compared to a conventional saliency point detection method (SIFT) in terms of computational time, quality of the point detection and registration accuracy. Our method achieved similar accuracy and similar saliency detection quality in < 5% the detection time, showing promising capabilities towards real-time whole-body fetal ultrasound imaging.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Gomez, A., Bhatia, K., Tharin, S., Housden, J., Toussaint, N., & Schnabel, J.A. (2017). Fast registration of 3D fetal ultrasound images using learned corresponding salient points. In M. Jorge Cardoso, Tal Arbel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 33-41). Quebec City, QC, CAN: Springer Verlag.
MLA:
Gomez, Alberto, et al. "Fast registration of 3D fetal ultrasound images using learned corresponding salient points." Proceedings of the International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. M. Jorge Cardoso, Tal Arbel, Springer Verlag, 2017. 33-41.
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