Human age estimation with surface-based features from MRI images
Wang, Jieqiong; Dai, Dai; Li, Meng; Hua, Jing; *He, Huiguang
2012
Conference Name3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Source Publication3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Conference Date2012/10/1
Conference Place美国
AbstractOver the past years, many efforts have been made in the estimation of the physiological age based on the human MRI brain images. In this paper, we propose a novel regression model with surface-based features to estimate the human age automatically and accurately. First, individual regional surface-based features (thickness, mean curvature, Gaussian curvature and surface area) from the MRI image were extracted, which were subsequently used to construct combined regional features and the brain networks. Then, the individual regional surface-based features, brain network with surface-based features and combined regional surface-based features were used for age regression by relevance vector machine (RVM), respectively. In the experiment, a dataset of 360 healthy subjects aging from 20 to 82 years was used to evaluate the performance. Experimental results based on 10-fold cross validation show that, compared to the previous methods, age estimation model with combined surface-based features can yield a remarkably high accuracy (mean absolute error: 4.6 years and root mean squared error: 5.6 years) and a significantly high correlation coefficient (r = 0.94), which is the best age estimation result as far as we know and suggests that surface-based features are more powerful than other features used in previous methods for human age estimation.
KeywordAge Estimation Age-based Brain Images Brain Networks Correlation Coefficient Cross Validation Data Sets Gaussian Curvatures Healthy Subjects Human Age Estimation Mean Absolute Error Mean Curvature Mri Image Regional Feature Regression Model Relevance Vector Machine Root Mean Squared Errors Surface Area Surface-based
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20555
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Recommended Citation
GB/T 7714
Wang, Jieqiong,Dai, Dai,Li, Meng,et al. Human age estimation with surface-based features from MRI images[C],2012.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Jieqiong]'s Articles
[Dai, Dai]'s Articles
[Li, Meng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Jieqiong]'s Articles
[Dai, Dai]'s Articles
[Li, Meng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Jieqiong]'s Articles
[Dai, Dai]'s Articles
[Li, Meng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.