2015 | OriginalPaper | Buchkapitel
Structured Decision Forests for Multi-modal Ultrasound Image Registration
verfasst von : Ozan Oktay, Andreas Schuh, Martin Rajchl, Kevin Keraudren, Alberto Gomez, Mattias P. Heinrich, Graeme Penney, Daniel Rueckert
Erschienen in: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
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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.