{"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}
引用次数: 8
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.