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Open Access Green as soon as Postprint is submitted to ZB.
Fast registration of 3D fetal ultrasound images using learned corresponding salient points.
In: (International Workshop on Ophthalmic Medical Image Analysis). Berlin [u.a.]: Springer, 2017. 33-41 (Lect. Notes Comput. Sc. ; 10554 LNCS)
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.
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Publication type
Article: Conference contribution
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
International Workshop on Ophthalmic Medical Image Analysis
Quellenangaben
Volume: 10554 LNCS,
Pages: 33-41
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)