Automated segmentation of MS lesions from multi-channel MR images

被引：0
|
作者：
Van Leemput, K
Maes, F
Bello, F
Vandermeulen, D
Colchester, A
Suetens, P
机构：
[1] Katholieke Univ Leuven, ESAT, UZ Gasthuisberg, Med Image Comp, B-3000 Louvain, Belgium
[2] Univ Kent, Elect Engn Labs, Neurosci Med Image Anal Grp, Canterbury CT2 7NT, Kent, England
来源：
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, MICCAI'99, PROCEEDINGS | 1999年 / 1679卷
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Quantitative analysis of AIR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel AIR images. The method simultaneously corrects for AIR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels. MS lesions are detected as voxels that are not well explained by the model. The results of the automated method are compared with the lesions delineated by human experts, showing a significant total lesion load correlation and an average overall spatial correspondence similar to that between the experts.
引用
收藏
页码：11 / 21
页数：11
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