Estimation of Shooting Part Using a Camera with Depth Sensors and Pose Estimation Method and Automatic Setting of Optimal X-ray Imaging Conditions
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- Eto Michihiro
- Doctoral Program, Graduate School of Engineering, Oita University Department of Radiological Technology, Nippon Bunri University Medical College
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- Nakawatari Tomofumi
- Department of Radiological Technology, Nippon Bunri University Medical College
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- Hatanaka Yuji
- Division of Computer Science and Intelligent Systems, Faculty of Science and Technology, Oita University
Bibliographic Information
- Other Title
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- 深度計測センサ付きカメラと姿勢推定手法による撮影部位推定と最適撮影条件の自動設定の検討
- シンド ケイソク センサ ツキ カメラ ト シセイ スイテイ シュホウ ニ ヨル サツエイ ブイ スイテイ ト サイテキ サツエイ ジョウケン ノ ジドウ セッテイ ノ ケントウ
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Description
<p> Purpose: In this study, we propose a system that combines a depth camera with a deep learning model for estimating the human skeleton and a depth camera to estimate the shooting part to be radiographed and to acquire the thickness of the subject, thereby providing optimized X-ray imaging conditions. Methods: We propose a system that provides optimized X-ray imaging conditions by estimating the shooting part and measuring the thickness of the subject using an RGB camera and a depth camera. The system uses OpenPose, a posture estimation library, to estimate the shooting part. Results: The recognition rate of the shooting part was 15.38% for the depth camera and 84.62% for the RGB camera at a distance of 100 cm, and 42.31% for the depth camera and 100% for the RGB camera at a distance of 120 cm. The measurement accuracy of the subject thickness was within ±10 mm except for a few cases, indicating that the X-ray imaging conditions were optimized for the subject thickness. Conclusion: The implementation of this system in an X-ray system is expected to enable automatic setting of X-ray imaging conditions. The system is also useful in preventing increased exposure dose due to excessive dose or decreased image quality due to insufficient dose caused by incorrect setting of X-ray imaging conditions.</p>
Journal
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- Japanese Journal of Radiological Technology
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Japanese Journal of Radiological Technology 79 (5), 431-439, 2023
Japanese Society of Radiological Technology
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Details 詳細情報について
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- CRID
- 1390859138435894912
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- NII Book ID
- AN00197784
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- ISSN
- 18814883
- 03694305
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- NDL BIB ID
- 032900607
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- PubMed
- 36948627
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- Text Lang
- ja
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- Article Type
- journal article
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- Data Source
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- JaLC
- NDL Search
- Crossref
- PubMed
- OpenAIRE
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- Abstract License Flag
- Disallowed