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Paper Abstract and Keywords
Presentation 2018-11-06 10:40
Improvement of Laparoscopic Color Image Diagnosis for Automatic Detection of Coded Defect Region and Application of Effective Classifier Parameter
Norifumi Kawabata (Nagoya Univ.), Toshiya Nakaguchi (Chiba Univ.) MICT2018-42 MI2018-42
Abstract (in Japanese) (See Japanese page) 
(in English) In addition to judgment by human eyes up to the present, it is advanced for R&D of diagnostic imaging systems by using artificial intelligence positively. Particularly, from informatics, medical engineering field, it is needed for the automatic diagnosis based on image processing to achieve laparoscopic surgery support. Compared to images in the engineering field, it is difficult in the medical images for quantitative judgment and assessment of the coded defect and degradation. Therefore, we also need to consider color information including human visual characteristics and the application of classifier parameter for image information. In this study, first we judged using PSNR whether we can detect the coded defect or not after processing of H.265/HEVC for the certain region in frame images cut from laparoscopic video acquired using endoscopy. Next, we analyzed color information using S-CIELAB color space. Finally, we applied classifier parameter effectively, and discussed for medical image diagnosis.
Keyword (in Japanese) (See Japanese page) 
(in English) Peak Signal to Noise Ratio / S-CIELAB Color Space / CIEDE2000 / Support Vector Machine / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 286, MI2018-42, pp. 21-26, Nov. 2018.
Paper # MI2018-42 
Date of Issue 2018-10-30 (MICT, MI) 
ISSN 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)
Download PDF MICT2018-42 MI2018-42

Conference Information
Committee MICT MI  
Conference Date 2018-11-06 - 2018-11-06 
Place (in Japanese) (See Japanese page) 
Place (in English) University of Hyogo 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2018-11-MICT-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improvement of Laparoscopic Color Image Diagnosis for Automatic Detection of Coded Defect Region and Application of Effective Classifier Parameter 
Sub Title (in English)  
Keyword(1) Peak Signal to Noise Ratio  
Keyword(2) S-CIELAB Color Space  
Keyword(3) CIEDE2000  
Keyword(4) Support Vector Machine  
1st Author's Name Norifumi Kawabata  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Toshiya Nakaguchi  
2nd Author's Affiliation Chiba University (Chiba Univ.)
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Speaker Author-1 
Date Time 2018-11-06 10:40:00 
Presentation Time 20 minutes 
Registration for MI 
Paper # MICT2018-42, MI2018-42 
Volume (vol) vol.118 
Number (no) no.285(MICT), no.286(MI) 
Page pp.21-26 
Date of Issue 2018-10-30 (MICT, MI) 

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