Paper Info
Title
Structured Decision Forests for Multi-modal Ultrasound Image Registration.
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.
Year
DOI
Venue
2015
10.1007/978-3-319-24571-3_44
Lecture Notes in Computer Science
Field
DocType
Volume
Computer vision,Pattern recognition,Computer science,Imaging modalities,Image representation,Image quality,Artificial intelligence,Probabilistic logic,Random forest,Image registration,Modal,Ultrasound image
Conference
9350
ISSN
Citations 
PageRank 
0302-9743
2
0.43
References 
Authors
11
8
Name
Order
Citations
PageRank
Ozan Oktay128020.15
Andreas Schuh2786.89
Martin Rajchl342134.67
Kevin Keraudren4846.98
Alberto Gomez55012.62
Mattias P. Heinrich687353.64
Graeme P. Penney798790.21
Daniel Rueckert89338637.58