ML Anthology

Human Brain Labeling Using Image Similarities

François Rousseau, Piotr A. Habas, Colin Studholme
CVPR 2011 pp. 1081-1088
doi:10.1109/CVPR.2011.5995694 /cvpr/2011/rousseau2011cvpr-human/

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.

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Cite

Text

Rousseau et al. "Human Brain Labeling Using Image Similarities." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995694

Markdown

[Rousseau et al. "Human Brain Labeling Using Image Similarities." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/rousseau2011cvpr-human/) doi:10.1109/CVPR.2011.5995694

BibTeX

@inproceedings{rousseau2011cvpr-human,
  title     = {{Human Brain Labeling Using Image Similarities}},
  author    = {Rousseau, François and Habas, Piotr A. and Studholme, Colin},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {1081-1088},
  doi       = {10.1109/CVPR.2011.5995694},
  url       = {https://mlanthology.org/cvpr/2011/rousseau2011cvpr-human/}
}