Combining binary classifiers for automatic cartilage segmentation in knee MRI

被引:0
|
作者
Folkesson, J [1 ]
Olsen, OF
Pettersen, P
Dam, E
Christiansen, C
机构
[1] IT Univ Copenhagen, Image Anal Grp, Copenhagen, Denmark
[2] Ctr Clin & Basic Res, Ballerup, Denmark
来源
COMPUTER VISION FOR BIOMEDICAL IMAGE APPLICATIONS, PROCEEDINGS | 2005年 / 3765卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We have developed a method for segmenting tibial and femoral medial cartilage in MR knee scans by combining two k Nearest Neighbors (kNN) classifiers for the cartilage classes with a rejection threshold for the background class. We show that with this threshold, two binary classifiers are sufficient compared to three binary classifiers in the traditional one-versus-all approach. We also show that the combination of binary classifiers produces better results than a kNN classifier that is trained to partition the voxels directly into three classes. The resulting sensitivity, specificity and Dice volume overlap of our method are 84.2%, 99.9% and 0.81 respectively. Compared to state-of-the-art segmentation methods, our method outperforms a fully automatic method and is comparable to a semi-automatic method.
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页码:230 / +
页数:3
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