{"title":"基于fBm模型和GLC矩阵的牙槽骨丢失区定位","authors":"P. Lin, P. Huang, P. Huang, H. Hsu, Ping Chen","doi":"10.1109/ISBB.2014.6820947","DOIUrl":null,"url":null,"abstract":"We propose an effective method to detect alveolar bone-loss areas in dental periapical radiographs in this paper. By analyzing the texture of alveolar bone tissues measured by Gray Level Co-occurrence Matrix (GLCM) or the H-value of fractal Brownian motions (fBm) model, we transfer radiograph images into bone-texture images. Then by auto-thresholding, we segment the bone-texture images into normal and bone-loss regions. Experimental results on six periapical images demonstrate that our method using fBm-H value as the texture feature can detect bone-loss areas best conforming to the areas marked by a dentist both visually and quantitatively among all the features used.","PeriodicalId":265886,"journal":{"name":"2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Alveolar bone-loss area localization in periapical radiographs by texture analysis based on fBm model and GLC matrix\",\"authors\":\"P. Lin, P. Huang, P. Huang, H. Hsu, Ping Chen\",\"doi\":\"10.1109/ISBB.2014.6820947\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an effective method to detect alveolar bone-loss areas in dental periapical radiographs in this paper. By analyzing the texture of alveolar bone tissues measured by Gray Level Co-occurrence Matrix (GLCM) or the H-value of fractal Brownian motions (fBm) model, we transfer radiograph images into bone-texture images. Then by auto-thresholding, we segment the bone-texture images into normal and bone-loss regions. Experimental results on six periapical images demonstrate that our method using fBm-H value as the texture feature can detect bone-loss areas best conforming to the areas marked by a dentist both visually and quantitatively among all the features used.\",\"PeriodicalId\":265886,\"journal\":{\"name\":\"2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBB.2014.6820947\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBB.2014.6820947","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Alveolar bone-loss area localization in periapical radiographs by texture analysis based on fBm model and GLC matrix
We propose an effective method to detect alveolar bone-loss areas in dental periapical radiographs in this paper. By analyzing the texture of alveolar bone tissues measured by Gray Level Co-occurrence Matrix (GLCM) or the H-value of fractal Brownian motions (fBm) model, we transfer radiograph images into bone-texture images. Then by auto-thresholding, we segment the bone-texture images into normal and bone-loss regions. Experimental results on six periapical images demonstrate that our method using fBm-H value as the texture feature can detect bone-loss areas best conforming to the areas marked by a dentist both visually and quantitatively among all the features used.