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Paper Abstract and Keywords
Presentation 2016-01-20 13:05
An Image-based Classification Algorithm for the Non-invasive Quality Evaluation of Confluent Cells
Kazuki Sato (Yamagata Univ.), Hiroto Sasaki, Ryuji Kato (Nagoya Univ.), Tetsuya Yuasa, Siu Kang (Yamagata Univ.) MI2015-130
Abstract (in Japanese) (See Japanese page) 
(in English) With recent progress of cell and tissue therapy in regenerative medicine, novel classification method of cell quality has been expected toward safety clinical application. We have developed the automatic image-based algorithm for the normal human dermal fibroblasts (NDHF) cells. The quality assessment of confluent NHDFs has inevitable difficulty due to their high density and strong heterogeneity of orientation distribution. It is also difficult to capture the morphological outlines of individual confluent cells. In order to quantify the population property of cell orientations, we performed the feature extraction based on the scale-invariance feature transform (SIFT) after some pre-processes. Through the learning by kernel support vector machine (kSVM) for the feature values, we tried to discriminate the remaining lifespan of cells and manipulation error during culturing. As a result, we demonstrated that our classification algorithm showed high accuracy and robustness for the assessments of cell quality without any invasive treatment. These results suggest that present study could provide reproducible and automatic classification algorithm of cell quality for future clinical application in tissue engineering fields.
Keyword (in Japanese) (See Japanese page) 
(in English) Regenerative medicine / Phase-contrast microscope / Normal human dermal fibroblasts (NHDF) / Cellular image / Scale-Invariant Feature Transform (SIFT) / Support Vector Machine (SVM) / pattern recognition /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 401, MI2015-130, pp. 277-282, Jan. 2016.
Paper # MI2015-130 
Date of Issue 2016-01-12 (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 MI2015-130

Conference Information
Committee MI  
Conference Date 2016-01-19 - 2016-01-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Bunka Tenbusu Kan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General topics in medical imaging 
Paper Information
Registration To MI 
Conference Code 2016-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Image-based Classification Algorithm for the Non-invasive Quality Evaluation of Confluent Cells 
Sub Title (in English)  
Keyword(1) Regenerative medicine  
Keyword(2) Phase-contrast microscope  
Keyword(3) Normal human dermal fibroblasts (NHDF)  
Keyword(4) Cellular image  
Keyword(5) Scale-Invariant Feature Transform (SIFT)  
Keyword(6) Support Vector Machine (SVM)  
Keyword(7) pattern recognition  
Keyword(8)  
1st Author's Name Kazuki Sato  
1st Author's Affiliation Yamagata University (Yamagata Univ.)
2nd Author's Name Hiroto Sasaki  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Ryuji Kato  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Tetsuya Yuasa  
4th Author's Affiliation Yamagata University (Yamagata Univ.)
5th Author's Name Siu Kang  
5th Author's Affiliation Yamagata University (Yamagata Univ.)
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Speaker Author-1 
Date Time 2016-01-20 13:05:00 
Presentation Time 40 minutes 
Registration for MI 
Paper # MI2015-130 
Volume (vol) vol.115 
Number (no) no.401 
Page pp.277-282 
#Pages 6 
Date of Issue 2016-01-12 (MI) 

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