Human brain labeling using image similarities

Authors:
F. Rousseau;P. A. Habas;C. Studholme
Affiliations:
LSIIT, Univ. Strasbourg, Strasbourg, France;Dept. of Pediatrics, Univ. of Washington, Seattle, WA, USA;Dept. of Pediatrics, Univ. of Washington, Seattle, WA, USA
Venue:
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Year:
2011

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Abstract

We propose in this work a patch-based segmentation method relying on a label propagation framework. Based on image intensity similarities between the input image and a learning dataset, an original strategy which does not require any non-rigid registration is presented. Following recent developments in non-local image denoising, the similarity between images is represented by a weighted graph computed from intensity-based distance between patches. Experiments on simulated and in-vivo MR images show that the proposed method is very successful in providing automated human brain labeling.