Abstract
Interventional procedures in cardiovascular diseases often require ultrasound (US) image guidance. These US images must be combined with pre-operatively acquired tomographic images to provide a roadmap for the intervention. Spatial alignment of pre-operative images with intra-operative US images can provide valuable clinical information. Existing multi-modal US registration techniques often do not achieve reliable registration due to low US image quality. To address this problem, a novel medical image representation based on a trained decision forest named probabilistic edge map (PEM) is proposed in this paper. PEMs are generic and modality-independent. They generate similar anatomical representations from different imaging modalities and can thus guide a multi-modal image registration algorithm more robustly and accurately. The presented image registration framework is evaluated on a clinical dataset consisting of 10 pairs of 3D US-CT and 7 pairs of 3D US-MR cardiac images. The experiments show that a registration based on PEMs is able to estimate more reliable and accurate inter-modality correspondences compared to other state-of-the-art US registration methods.
Originalsprache | Englisch |
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Titel | Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 |
Redakteure/-innen | Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro Frangi |
Seitenumfang | 9 |
Band | 9350 |
Herausgeber (Verlag) | Springer Vieweg, Berlin Heidelberg |
Erscheinungsdatum | 20.11.2015 |
Seiten | 363-371 |
ISBN (Print) | 978-3-319-24570-6 |
ISBN (elektronisch) | 978-3-319-24571-3 |
DOIs | |
Publikationsstatus | Veröffentlicht - 20.11.2015 |
Veranstaltung | 18th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - Munich, Deutschland Dauer: 05.10.2015 → 09.10.2015 https://www.miccai2015.org/ |