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Paper Abstract and Keywords
Presentation 2018-03-19 15:25
Feature-selection method based on Grassmann distance for the classification of neoplastic polyps on endocytoscopic images
Hayato Itoh (Nagoya Univ.), Yuichi Mori, Masashi Misawa (Showa Univ.), Masahiro Oda (Nagoya Univ.), Shin-ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) MI2017-81
Abstract (in Japanese) (See Japanese page) 
(in English) An endocytoscope provides ultramagnified observation that enable physicians to achieve minimally invasive and real-time diagnosis in colonoscopy. However, much pathological knowledge and clinical experiences are required for this diagnosis. The computer-aided diagnosis (CAD) system that decreases overlooking of neoplastic polyps in endocytoscopy is required. Texture-feature-based classification between neoplastic and non-neoplastic images of polyps has been proposed towards the construction of the CAD system. Extracted texture feature includes worthless elements that do not contribute to classification. This worthless elements can lead classification errors. We propose feature-selection method that selects discriminative feature for two-categories classification by omitting worthless feature of texture features. We experimentally evaluated the proposed method by comparing the classification accuracy between before and after feature selection with about 19,000 endocytoscopic images. The experimental results show that our proposed method gives 2.4% higher accuracy than original texture feature.
Keyword (in Japanese) (See Japanese page) 
(in English) endocytoscopy / automated pathological diagnosis / feature selection / texture feature / manifold learning / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 518, MI2017-81, pp. 51-56, March 2018.
Paper # MI2017-81 
Date of Issue 2018-03-12 (MI) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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 SIP EA SP MI  
Conference Date 2018-03-19 - 2018-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics [SIP, EA, SP]/ Medical Image Engineering, Analysis, Recognition, etc. [MI] 
Paper Information
Registration To MI 
Conference Code 2018-03-SIP-EA-SP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Feature-selection method based on Grassmann distance for the classification of neoplastic polyps on endocytoscopic images 
Sub Title (in English)  
Keyword(1) endocytoscopy  
Keyword(2) automated pathological diagnosis  
Keyword(3) feature selection  
Keyword(4) texture feature  
Keyword(5) manifold learning  
1st Author's Name Hayato Itoh  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Yuichi Mori  
2nd Author's Affiliation Showa University Northern Yokohama Hospital (Showa Univ.)
3rd Author's Name Masashi Misawa  
3rd Author's Affiliation Showa University Northern Yokohama Hospital (Showa Univ.)
4th Author's Name Masahiro Oda  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
5th Author's Name Shin-ei Kudo  
5th Author's Affiliation Showa University Northern Yokohama Hospital (Showa Univ.)
6th Author's Name Kensaku Mori  
6th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2018-03-19 15:25:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2017-81 
Volume (vol) vol.117 
Number (no) no.518 
Page pp.51-56 
Date of Issue 2018-03-12 (MI) 

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