Human brain labeling using image similarities
Francois Waldeck Rousseau, Piotr A. Habas, Colin Studholme · 2011
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.