基于假体检测的x射线牙齿全景图像中的牙齿识别

Kazunori Oka, Anas M. Ali, Daisuke Fujita, Syoji Kobashi
{"title":"基于假体检测的x射线牙齿全景图像中的牙齿识别","authors":"Kazunori Oka, Anas M. Ali, Daisuke Fujita, Syoji Kobashi","doi":"10.1109/ICMLC56445.2022.9941333","DOIUrl":null,"url":null,"abstract":"In the current dental practice, many panoramic dental images of the oral cavity are taken by x-ray radiograph. Using the dental panoramic images, a physician or dental assistant records dental chart. These burdens can deteriorate the quality of medical care, such as erroneous entries. Therefore, automatic analysis of panoramic dental images is desired. We have previously proposed a teeth recognition method based on Faster R-CNN and an optimization approach that performed a 94.2% accuracy. However, it shows a relatively low accuracy in panoramic images with prostheses. This paper proposed a new method to improve the accuracy by detecting prostheses separately. It first detects four types of prosthetic teeth using YOLOv5. Then, it recognizes the teeth and the prosthetic teeth simultaneously based on the proposed optimization approach using a prior knowledge model. The proposed method achieved a maximum recognition accuracy of 97.17%. It shows the usefulness of optimization using prior knowledge models in combination with prosthetic tooth detection.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Tooth Recognition in X-Ray Dental Panoramic Images with Prosthetic Detection\",\"authors\":\"Kazunori Oka, Anas M. Ali, Daisuke Fujita, Syoji Kobashi\",\"doi\":\"10.1109/ICMLC56445.2022.9941333\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the current dental practice, many panoramic dental images of the oral cavity are taken by x-ray radiograph. Using the dental panoramic images, a physician or dental assistant records dental chart. These burdens can deteriorate the quality of medical care, such as erroneous entries. Therefore, automatic analysis of panoramic dental images is desired. We have previously proposed a teeth recognition method based on Faster R-CNN and an optimization approach that performed a 94.2% accuracy. However, it shows a relatively low accuracy in panoramic images with prostheses. This paper proposed a new method to improve the accuracy by detecting prostheses separately. It first detects four types of prosthetic teeth using YOLOv5. Then, it recognizes the teeth and the prosthetic teeth simultaneously based on the proposed optimization approach using a prior knowledge model. The proposed method achieved a maximum recognition accuracy of 97.17%. It shows the usefulness of optimization using prior knowledge models in combination with prosthetic tooth detection.\",\"PeriodicalId\":117829,\"journal\":{\"name\":\"2022 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC56445.2022.9941333\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC56445.2022.9941333","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在目前的牙科实践中,许多口腔全景图像都是通过x光片拍摄的。利用牙科全景图像,医生或牙科助理记录牙科图表。这些负担会降低医疗服务的质量,例如错误的记录。因此,需要对牙科全景图像进行自动分析。我们之前提出了一种基于Faster R-CNN和优化方法的牙齿识别方法,其准确率为94.2%。然而,它在带有假体的全景图像中显示出相对较低的精度。本文提出了一种通过对假体进行单独检测来提高检测精度的新方法。它首先使用YOLOv5检测四种类型的假牙。在此基础上,利用先验知识模型实现了假牙和真牙的同时识别。该方法的最高识别准确率为97.17%。结果表明,将先验知识模型与义齿检测相结合进行优化是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Tooth Recognition in X-Ray Dental Panoramic Images with Prosthetic Detection
In the current dental practice, many panoramic dental images of the oral cavity are taken by x-ray radiograph. Using the dental panoramic images, a physician or dental assistant records dental chart. These burdens can deteriorate the quality of medical care, such as erroneous entries. Therefore, automatic analysis of panoramic dental images is desired. We have previously proposed a teeth recognition method based on Faster R-CNN and an optimization approach that performed a 94.2% accuracy. However, it shows a relatively low accuracy in panoramic images with prostheses. This paper proposed a new method to improve the accuracy by detecting prostheses separately. It first detects four types of prosthetic teeth using YOLOv5. Then, it recognizes the teeth and the prosthetic teeth simultaneously based on the proposed optimization approach using a prior knowledge model. The proposed method achieved a maximum recognition accuracy of 97.17%. It shows the usefulness of optimization using prior knowledge models in combination with prosthetic tooth detection.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Fast Semantic Segmentation for Vectorization of Line Drawings Based on Deep Neural Networks Real-Time Vehicle Counting by Deep-Learning Networks Unsupervised Representation Learning Method In Sensor Based Human Activity Recognition Improvement and Evaluation of Object Shape Presentation System Using Linear Actuators Examination of Analysis Methods for E-Learning System Grade Data Using Formal Concept Analysis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1