| Paper Abstract and Keywords |
| Presentation |
2016-08-08 13:30
Multi-stage identification of GGO candidate regions using DCNN and SVM
Kazuki Hirayama, Joo Kooi Tan, Hyoungseop Kim (KIT), Takatoshi Aoki (UOEH), Shoji Kido (YU) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Recently, the development of CAD (Computer Aided Diagnosis) system for the purpose of reducing the burden to the physicians and improving accuracy of lesion detection has been advanced. In this paper, we propose an automatic extraction method of GGO (Ground Glass Opacity) candidate regions from the chest CT image. The flow of the main processings are, extracting the lung area from the chest CT image, the removal of a blood vessel regions, bronchus region by 3D line enhancement filter. Then, we selected the initial GGO candidate regions by the density-gradient threshold processing, and identifies with DCNN (Deep Convolutional Neural Network) for that areas. Finally, we calculates the features, to reduce the false-positive shadow by the rule-based method, performs identification of the final GGO candidate regions by SVM (Support Vector Machine). |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Ground Glass Opacity / Computer Aided Diagnosis / Lung Image Database Consortium / Deep Convolutional Neural Network / Support Vector Machine / / / |
| Reference Info. |
ITE Tech. Rep. |
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| Conference Information |
| Committee |
ME CE IPSJ-AVM IEICE-IE |
| Conference Date |
2016-08-08 - 2016-08-09 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
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| Topics (in Japanese) |
(See Japanese page) |
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| Paper Information |
| Registration To |
IEICE-IE |
| Conference Code |
2016-08-IE-ME-AVM-CE |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Multi-stage identification of GGO candidate regions using DCNN and SVM |
| Sub Title (in English) |
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| Keyword(1) |
Ground Glass Opacity |
| Keyword(2) |
Computer Aided Diagnosis |
| Keyword(3) |
Lung Image Database Consortium |
| Keyword(4) |
Deep Convolutional Neural Network |
| Keyword(5) |
Support Vector Machine |
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| 1st Author's Name |
Kazuki Hirayama |
| 1st Author's Affiliation |
Kyushu Institute of Technology (KIT) |
| 2nd Author's Name |
Joo Kooi Tan |
| 2nd Author's Affiliation |
Kyushu Institute of Technology (KIT) |
| 3rd Author's Name |
Hyoungseop Kim |
| 3rd Author's Affiliation |
Kyushu Institute of Technology (KIT) |
| 4th Author's Name |
Takatoshi Aoki |
| 4th Author's Affiliation |
University of Occupational and Environmental Health (UOEH) |
| 5th Author's Name |
Shoji Kido |
| 5th Author's Affiliation |
Yamaguchi University (YU) |
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| Speaker |
Author-1 |
| Date Time |
2016-08-08 13:30:00 |
| Presentation Time |
30 minutes |
| Registration for |
IEICE-IE |
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| Volume (vol) |
vol.40 |
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