Paper Abstract and Keywords |
Presentation |
2016-07-25 15:15
A Study of an Efficient Method of Selecting Effective Brain Local Features for Age Estimation Ryuichi Fujimoto, Koichi Ito (Tohoku Univ.), Kai Wu (South China Univ.), Kazunori Sato, Yasuyuki Taki (Tohoku Univ.), Hiroshi Fukuda (Tohoku Medical And Pharmaceutical Univ.), Takafumi Aoki (Tohoku Univ.) MI2016-38 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Estimating the age of a subject by evaluating brain morphological changes leads diagnostic support
or early detection of disease like Alzheimer’s Disease. In the age estimation method using local features extracted
from T1-weighted MRI images, the accuracy of the age estimation increases when only using effective local features.
By using local features defined by finely divided atlas, estimating accuracy would increase. However, it takes too
much computation time because of the increasing of the number of local regions. To solve this problem, this paper
proposes an efficient method of selecting effective brain local features for age estimation, and evaluate performance
of the proposed method. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
MRI / T1-weighted image / age estimation / brain aging / local features / machine learning / relevance vector machine / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 160, MI2016-38, pp. 13-18, July 2016. |
Paper # |
MI2016-38 |
Date of Issue |
2016-07-18 (MI) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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