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}
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.