Anupama Bhan, Ayush Goyal, Harsh, Naveen Chauhan, Ching-Wei Wang
{"title":"基于特征线轮廓的咬翼放射成像中龋的自动检测","authors":"Anupama Bhan, Ayush Goyal, Harsh, Naveen Chauhan, Ching-Wei Wang","doi":"10.1109/ICMETE.2016.59","DOIUrl":null,"url":null,"abstract":"Dental caries is a bacterial infection that causes tooth decay and is amongst the most common incessant maladies of individuals around the world. Teeth are defenseless to this infection all through their lifetime especially when care is not taken for proper oral hygiene. It is significant to analyze the dental images in order to improve and quantify medical images for correct diagnosis. Caries or cavity is one of the most prevalent diseases of the teeth. Dentists are putting the best effort to identify the problem at an earlier stage. The proposed method used in this paper is focused on the challenges faced during the cavity detection which sometimes is very tedious task due to small lesions not visible to human eye. The image processing techniques helps to identify the caries that provide dentists with the precise results of the area affected by the caries. The proposed methodology consists of preprocessing of bitewing radiographic images followed by edge recognition, thresholding and connected component labelling. This combinational approach provides qualitative and quantitative assessment to dentists on the presence of cavity. The caries are detected by connected component and mask overlap helps to highlight the affected area to grade the severity which is tested on the basis of line intensity profiles. Preparatory experiments show the significance of the proposed method to extract cavity and grade its effect on the tooth.","PeriodicalId":167368,"journal":{"name":"2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)","volume":"6 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Feature Line Profile Based Automatic Detection of Dental Caries in Bitewing Radiography\",\"authors\":\"Anupama Bhan, Ayush Goyal, Harsh, Naveen Chauhan, Ching-Wei Wang\",\"doi\":\"10.1109/ICMETE.2016.59\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dental caries is a bacterial infection that causes tooth decay and is amongst the most common incessant maladies of individuals around the world. Teeth are defenseless to this infection all through their lifetime especially when care is not taken for proper oral hygiene. It is significant to analyze the dental images in order to improve and quantify medical images for correct diagnosis. Caries or cavity is one of the most prevalent diseases of the teeth. Dentists are putting the best effort to identify the problem at an earlier stage. The proposed method used in this paper is focused on the challenges faced during the cavity detection which sometimes is very tedious task due to small lesions not visible to human eye. The image processing techniques helps to identify the caries that provide dentists with the precise results of the area affected by the caries. The proposed methodology consists of preprocessing of bitewing radiographic images followed by edge recognition, thresholding and connected component labelling. This combinational approach provides qualitative and quantitative assessment to dentists on the presence of cavity. The caries are detected by connected component and mask overlap helps to highlight the affected area to grade the severity which is tested on the basis of line intensity profiles. Preparatory experiments show the significance of the proposed method to extract cavity and grade its effect on the tooth.\",\"PeriodicalId\":167368,\"journal\":{\"name\":\"2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)\",\"volume\":\"6 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMETE.2016.59\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMETE.2016.59","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature Line Profile Based Automatic Detection of Dental Caries in Bitewing Radiography
Dental caries is a bacterial infection that causes tooth decay and is amongst the most common incessant maladies of individuals around the world. Teeth are defenseless to this infection all through their lifetime especially when care is not taken for proper oral hygiene. It is significant to analyze the dental images in order to improve and quantify medical images for correct diagnosis. Caries or cavity is one of the most prevalent diseases of the teeth. Dentists are putting the best effort to identify the problem at an earlier stage. The proposed method used in this paper is focused on the challenges faced during the cavity detection which sometimes is very tedious task due to small lesions not visible to human eye. The image processing techniques helps to identify the caries that provide dentists with the precise results of the area affected by the caries. The proposed methodology consists of preprocessing of bitewing radiographic images followed by edge recognition, thresholding and connected component labelling. This combinational approach provides qualitative and quantitative assessment to dentists on the presence of cavity. The caries are detected by connected component and mask overlap helps to highlight the affected area to grade the severity which is tested on the basis of line intensity profiles. Preparatory experiments show the significance of the proposed method to extract cavity and grade its effect on the tooth.