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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
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2016-38

Conference Information
Committee MI  
Conference Date 2016-07-25 - 2016-07-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Tomakomai Civic Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging, etc. 
Paper Information
Registration To MI 
Conference Code 2016-07-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of an Efficient Method of Selecting Effective Brain Local Features for Age Estimation 
Sub Title (in English)  
Keyword(1) MRI  
Keyword(2) T1-weighted image  
Keyword(3) age estimation  
Keyword(4) brain aging  
Keyword(5) local features  
Keyword(6) machine learning  
Keyword(7) relevance vector machine  
Keyword(8)  
1st Author's Name Ryuichi Fujimoto  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Koichi Ito  
2nd Author's Affiliation Tohoku University (Tohoku Univ.)
3rd Author's Name Kai Wu  
3rd Author's Affiliation South China University of Technology (South China Univ.)
4th Author's Name Kazunori Sato  
4th Author's Affiliation Tohoku University (Tohoku Univ.)
5th Author's Name Yasuyuki Taki  
5th Author's Affiliation Tohoku University (Tohoku Univ.)
6th Author's Name Hiroshi Fukuda  
6th Author's Affiliation Tohoku Medical And Pharmaceutical University (Tohoku Medical And Pharmaceutical Univ.)
7th Author's Name Takafumi Aoki  
7th Author's Affiliation Tohoku University (Tohoku Univ.)
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Speaker
Date Time 2016-07-25 15:15:00 
Presentation Time 30 
Registration for MI 
Paper # IEICE-MI2016-38 
Volume (vol) IEICE-116 
Number (no) no.160 
Page pp.13-18 
#Pages IEICE-6 
Date of Issue IEICE-MI-2016-07-18 


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