A supervised patch-based approach for human brain labeling.
A supervised patch-based approach for human brain labeling.
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DOI：
10.1109/tmi.2011.2156806
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发表时间：
2011-10
影响因子：
10.6
通讯作者：
Studholme C
中科院分区：
文献类型：
--
作者：
Rousseau F;Habas PA;Studholme C
在这项工作中，我们提出了一种基于标记传播框架的基于面片的图像标记方法。基于输入图像和解剖学教科书之间的图像强度相似性，提出了一种不需要任何非刚性配准的原始策略。随着非局部图像去噪的最新进展，图像之间的相似性由基于强度的块之间距离计算的加权图来表示。在模拟和活体磁共振图像上的实验表明，该方法在提供自动人脑标记方面是非常成功的。
We propose in this work a patch-based image labeling method relying on a label propagation framework. Based on image intensity similarities between the input image and an anatomy textbook, 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 an 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.