A Supervised Patch-Based Approach for Human Brain Labeling

被引：210

作者：

Rousseau, Franccois ^{[1
]}

Habas, Piotr A. ^{[2
]}

Studholme, Colin ^{[2
]}

机构：

[1] Univ Strasbourg, CNRS, UMR 7005, LSIIT, F-67412 Illkirch Graffenstaden, France

[2] Univ Washington, Biomed Image Comp Grp, Seattle, WA 98195 USA

来源：

IEEE TRANSACTIONS ON MEDICAL IMAGING | 2011年 / 30卷 / 10期

基金：

欧洲研究理事会; 美国国家卫生研究院;

关键词：

Brain magnetic resonance imaging (MRI); image segmentation; label propagation; nonlocal approach; ATLAS-BASED SEGMENTATION; MAGNETIC-RESONANCE IMAGES; AUTOMATIC SEGMENTATION; FUSION; MRI; REGISTRATION; HIPPOCAMPUS; COMBINATION; ALGORITHMS; TRANSFORM;

D O I：

10.1109/TMI.2011.2156806

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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 nonrigid registration is presented. Following recent developments in nonlocal 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 magnetic resonance images show that the proposed method is very successful in providing automated human brain labeling.

引用

页码：1852 / 1862

页数：11

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