I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, E. Lespessailles, R. Jennane, M. H. Bedoui
{"title":"利用腔隙测量的骨小梁x线片特征","authors":"I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, E. Lespessailles, R. Jennane, M. H. Bedoui","doi":"10.1109/CGIV.2016.45","DOIUrl":null,"url":null,"abstract":"Osteoporosis is a disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The goal is to analyse according to clinical research a group of 174 subjects: 87 osteoporotic patients with various bone fracture types and 87 healthy subjects. In order to characterize osteoporosis, a method of lacunarity measurement for grayscale image is used. This approach allowed the discrimination between healthy subjects and patients with osteoporosis. The results show an improved classification rate compared to another work [1].","PeriodicalId":351561,"journal":{"name":"2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Trabecular Bone Radiograph Characterization Using Lacunarity Measurement\",\"authors\":\"I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, E. Lespessailles, R. Jennane, M. H. Bedoui\",\"doi\":\"10.1109/CGIV.2016.45\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Osteoporosis is a disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The goal is to analyse according to clinical research a group of 174 subjects: 87 osteoporotic patients with various bone fracture types and 87 healthy subjects. In order to characterize osteoporosis, a method of lacunarity measurement for grayscale image is used. This approach allowed the discrimination between healthy subjects and patients with osteoporosis. The results show an improved classification rate compared to another work [1].\",\"PeriodicalId\":351561,\"journal\":{\"name\":\"2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CGIV.2016.45\",\"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 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CGIV.2016.45","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Trabecular Bone Radiograph Characterization Using Lacunarity Measurement
Osteoporosis is a disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The goal is to analyse according to clinical research a group of 174 subjects: 87 osteoporotic patients with various bone fracture types and 87 healthy subjects. In order to characterize osteoporosis, a method of lacunarity measurement for grayscale image is used. This approach allowed the discrimination between healthy subjects and patients with osteoporosis. The results show an improved classification rate compared to another work [1].