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Paper Abstract and Keywords
Presentation 2014-01-26 16:35
Random-forest based spatial normalization using anatomical landmarks from CT images with unknown FOV
Kazuma Kikuchi, Toshiyuki Okada (Osaka Univ.), Shouhei Hanaoka, Yoshitaka Masutani (Univ. of Tokyo), Masatoshi Hori, Noriyuki Tomiyama, Yoshinobu Sato (Osaka Univ.) MI2013-87
Abstract (in Japanese) (See Japanese page) 
(in English) We have proposed the fully automated multi-organ segmentation using computational anatomy modelfrom abdominal CT images. However, this method cannot apply to CT data with different eld of view (FOV) dueto the spatial normalization based on assumption that the FOV of CT image is upper abdomen. In this paper,we propose a method for spatial normalization using anatomical landmarks from CT images with unknown FOV.Given a number of anatomical landmarks, the parameters of the bounding box which determines the normalizationspace are estimated using random forest. The proposed method was evaluated by comparing the segmentationaccuracy using spatial normalization with known FOV. The experimental results using 35 thoracoabdominal CTimages showed that the performance of the proposed method was comparable to the segmentation using spatialnormalization with known FOV.
Keyword (in Japanese) (See Japanese page) 
(in English) machine learning / importance / multiple organ segmentation / feature selection / / / /  
Reference Info. IEICE Tech. Rep., vol. 113, no. 410, MI2013-87, pp. 169-173, Jan. 2014.
Paper # MI2013-87 
Date of Issue 2014-01-19 (MI) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee MI  
Conference Date 2014-01-26 - 2014-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Bunka Tenbusu Kan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Computer Assisted Diagnosis and Therapy Based on Computational Anatomy, etc. 
Paper Information
Registration To MI 
Conference Code 2014-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Random-forest based spatial normalization using anatomical landmarks from CT images with unknown FOV 
Sub Title (in English)  
Keyword(1) machine learning  
Keyword(2) importance  
Keyword(3) multiple organ segmentation  
Keyword(4) feature selection  
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1st Author's Name Kazuma Kikuchi  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Toshiyuki Okada  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Shouhei Hanaoka  
3rd Author's Affiliation University of Tokyo (Univ. of Tokyo)
4th Author's Name Yoshitaka Masutani  
4th Author's Affiliation University of Tokyo (Univ. of Tokyo)
5th Author's Name Masatoshi Hori  
5th Author's Affiliation Osaka University (Osaka Univ.)
6th Author's Name Noriyuki Tomiyama  
6th Author's Affiliation Osaka University (Osaka Univ.)
7th Author's Name Yoshinobu Sato  
7th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2014-01-26 16:35:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2013-87 
Volume (vol) vol.113 
Number (no) no.410 
Page pp.169-173 
#Pages
Date of Issue 2014-01-19 (MI) 


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