{"title":"不同自动聚焦方法在明场显微镜图像中检测结核分枝杆菌的比较研究","authors":"Gagan Saini, R. Panicker, B. Soman, Jeny Rajan","doi":"10.1109/DISCOVER.2016.7806223","DOIUrl":null,"url":null,"abstract":"Automatic tuberculosis (TB) detection methods using microscopic images are becoming more popular now a days. Auto-focusing is the first and foremost step in the development of an automated microscope for TB detection. Different focus measures exist for the selection of in-focus image from both fluorescence and bright field microscopic images. Recently, some researchers have investigated and compared several different focus measures for TB sputum microscopy. In this study we focused on bright field microscopic images and considered around 20 popular focus measures. Experiments were conducted on a large set of images having different features.","PeriodicalId":383554,"journal":{"name":"2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A comparative study of different auto-focus methods for mycobacterium tuberculosis detection from brightfield microscopic images\",\"authors\":\"Gagan Saini, R. Panicker, B. Soman, Jeny Rajan\",\"doi\":\"10.1109/DISCOVER.2016.7806223\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic tuberculosis (TB) detection methods using microscopic images are becoming more popular now a days. Auto-focusing is the first and foremost step in the development of an automated microscope for TB detection. Different focus measures exist for the selection of in-focus image from both fluorescence and bright field microscopic images. Recently, some researchers have investigated and compared several different focus measures for TB sputum microscopy. In this study we focused on bright field microscopic images and considered around 20 popular focus measures. Experiments were conducted on a large set of images having different features.\",\"PeriodicalId\":383554,\"journal\":{\"name\":\"2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DISCOVER.2016.7806223\",\"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 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DISCOVER.2016.7806223","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A comparative study of different auto-focus methods for mycobacterium tuberculosis detection from brightfield microscopic images
Automatic tuberculosis (TB) detection methods using microscopic images are becoming more popular now a days. Auto-focusing is the first and foremost step in the development of an automated microscope for TB detection. Different focus measures exist for the selection of in-focus image from both fluorescence and bright field microscopic images. Recently, some researchers have investigated and compared several different focus measures for TB sputum microscopy. In this study we focused on bright field microscopic images and considered around 20 popular focus measures. Experiments were conducted on a large set of images having different features.