Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kanavti:2015:10.1007/978-3-319-24888-2_12,
author = {Kanavti, F and Tong, T and Misawa, K and Mori, K and Rueckert, D and Glocker, B},
doi = {10.1007/978-3-319-24888-2_12},
pages = {94--101},
publisher = {Springer International Publishing},
title = {Supervoxel classification forests for estimating pairwise image correspondences},
url = {http://dx.doi.org/10.1007/978-3-319-24888-2_12},
year = {2015}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - This paper proposes a general method for establishing pairwise correspondences, which is a fundamental problem in image analysis. The method consists of over-segmenting a pair of images into supervoxels. A forest classifier is then trained on one of the images, the source, by using supervoxel indices as voxelwise class labels. Applying the forest on the other image, the target, yields a supervoxel labelling which is then regularized using majority voting within the boundaries of the target’s supervoxels. This yields semi-dense correspondences in a fully automatic, efficient and robust manner. The advantage of our approach is that no prior information or manual annotations are required, making it suitable as a general initialisation component for various medical imaging tasks that require coarse correspondences, such as, atlas/patch-based segmentation, registration, and atlas construction. Our approach is evaluated on a set of 150 abdominal CT images. In this dataset we use manual organ segmentations for quantitative evaluation. In particular, the quality of the correspondences is determined in a label propagation setting. Comparison to other state-of-the-art methods demonstrate the potential of supervoxel classification forests for estimating image correspondences.
AU - Kanavti,F
AU - Tong,T
AU - Misawa,K
AU - Mori,K
AU - Rueckert,D
AU - Glocker,B
DO - 10.1007/978-3-319-24888-2_12
EP - 101
PB - Springer International Publishing
PY - 2015///
SN - 0302-9743
SP - 94
TI - Supervoxel classification forests for estimating pairwise image correspondences
UR - http://dx.doi.org/10.1007/978-3-319-24888-2_12
UR - http://hdl.handle.net/10044/1/26577
ER -