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Paper Abstract and Keywords
Presentation 2017-05-26 09:20
An Age Estimation Method Using Brain Local Features of Brain MRI Images and Its Performance Evaluation
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.) SIP2017-13 IE2017-13 PRMU2017-13 MI2017-13
Abstract (in Japanese) (See Japanese page) 
(in English) It is known that brain tissues have age-related morphological changes through a set of statistical analysis
using large-scale brain MRI image databases.
This fact allows us to estimate the age of a subject from brain
MRI images by evaluating brain morphological changes with healthy aging.
The age estimated from morphological changes of a human brain
can be used for diagnostic support and early identification
of brain disorders such as Alzheimer's Disease.
This paper proposes an age estimation method using local features
extracted from T1-weighted MRI images.
Local features are defined by regional volume calculated from 1,024 local
regions of GM, WM and CSF.
We also add cortical thickness parcellated by Destrieux atlas to
brain local features in order to improve the accuracy of
age estimation.
We evaluate performance of the proposed method using a large-scale dataset of healty Japanese.
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. 117, no. 50, MI2017-13, pp. 67-70, May 2017.
Paper # MI2017-13 
Date of Issue 2017-05-18 (SIP, IE, PRMU, MI) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
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 SIP2017-13 IE2017-13 PRMU2017-13 MI2017-13

Conference Information
Committee PRMU IE MI SIP  
Conference Date 2017-05-25 - 2017-05-26 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2017-05-PRMU-IE-MI-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Age Estimation Method Using Brain Local Features of Brain MRI Images and Its Performance Evaluation 
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 Author-1 
Date Time 2017-05-26 09:20:00 
Presentation Time 30 minutes 
Registration for MI 
Paper # SIP2017-13, IE2017-13, PRMU2017-13, MI2017-13 
Volume (vol) vol.117 
Number (no) no.47(SIP), no.48(IE), no.49(PRMU), no.50(MI) 
Page pp.67-70 
#Pages 4 
Date of Issue 2017-05-18 (SIP, IE, PRMU, MI) 

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