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| Presentation | 2015-05-15 14:00 A study on bronchus segmentation based on machine learning method from chest CT Image Qier Meng (Nagoya Univ.), Takayuki Kitasaka (AIT), Yukitaka Nimura, Yoshihoko Nakamura, Masahiro Oda, Kensaku Mori (Nagoya Univ.)  SIP2015-23 IE2015-23 PRMU2015-23 MI2015-23 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper presents a method for extracting bronchus regions from 3D chest CT images that uses a combination of region growing, and voxel classification based on and machine learning methods. Most of previous methods focus on tracing the bronchial tree by region growing algorithms, it always fails to trace the tree when the abnormal appears to interrupt such as lung tumors. Our method is mainly based on detecting the candidate voxels which have the bronchial features and implementing the SVM method to select the appropriate candidates. First, we combined two types of tube enhancement filters to detect the candidate region having line structures based on the Hessian analysis, and a modified RRF(Radial Reach Filter). Second, we calculate bronchial features derived from both image intensity and shape. At last, we classify the candidate voxels by a classifier which is trained by the SVM using the training dataset. We applied the proposed method to four cases of 3D chest CT images and showed that it could extract the bronchial tree more accuracy than the previous method. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | SVM / local intensity structure / Radial Reach Filter / Hessian analysis / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 115, no. 25, MI2015-23, pp. 121-126, May 2015. | |
| Paper # | MI2015-23 | |
| Date of Issue | 2015-05-07 (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 | SIP2015-23 IE2015-23 PRMU2015-23 MI2015-23 |
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| Conference Information | |
| Committee | PRMU MI IE SIP |
| Conference Date | 2015-05-14 - 2015-05-15 |
| 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 | 2015-05-PRMU-MI-IE-SIP |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A study on bronchus segmentation based on machine learning method from chest CT Image |
| Sub Title (in English) | |
| Keyword(1) | SVM |
| Keyword(2) | local intensity structure |
| Keyword(3) | Radial Reach Filter |
| Keyword(4) | Hessian analysis |
| Keyword(5) | |
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| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Qier Meng |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Takayuki Kitasaka |
| 2nd Author's Affiliation | Aichi Institute of Technology (AIT) |
| 3rd Author's Name | Yukitaka Nimura |
| 3rd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 4th Author's Name | Yoshihoko Nakamura |
| 4th Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 5th Author's Name | Masahiro Oda |
| 5th Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 6th Author's Name | Kensaku Mori |
| 6th Author's Affiliation | Nagoya University (Nagoya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2015-05-15 14:00:00 |
| Presentation Time | 30 minutes |
| Registration for | MI |
| Paper # | SIP2015-23, IE2015-23, PRMU2015-23, MI2015-23 |
| Volume (vol) | vol.115 |
| Number (no) | no.22(SIP), no.23(IE), no.24(PRMU), no.25(MI) |
| Page | pp.121-126 |
| #Pages | 6 |
| Date of Issue | 2015-05-07 (SIP, IE, PRMU, MI) |
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