Supervoxel classification forests for estimating pairwise image correspondences

Kanavati F, Tong T, Misawa K, Fujiwara M, Mori K, Rueckert D, Glocker B (2015)

Publication Type: Conference contribution

Publication year: 2015

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 9352

Pages Range: 94-101

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Munich, DEU

ISBN: 9783319248875

DOI: 10.1007/978-3-319-24888-2_12

Abstract

This paper proposes a general method for establishing pair- wise 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.

Involved external institutions

Aichi Cancer Center Research Institute JP Japan (JP) Nagoya University Hospital / 名古屋大学医学部附属病院 JP Japan (JP) Nagoya University / 名古屋大学 JP Japan (JP) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Kanavati, F., Tong, T., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D., & Glocker, B. (2015). Supervoxel classification forests for estimating pairwise image correspondences. In Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 94-101). Munich, DEU: Springer Verlag.

MLA:

Kanavati, Fahdi, et al. "Supervoxel classification forests for estimating pairwise image correspondences." Proceedings of the 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015, Munich, DEU Ed. Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang, Springer Verlag, 2015. 94-101.

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