A novel auto-fluorescence method for Dental plaque detection and classification
碩士 === 國立交通大學 === 電機工程學系 === 106 === Dental health is a problem that modern people must pay attention to. According to the Health Promotion administration, the probability of getting dental caries is 88%, and those with periodontal disease such as periodontitis and gingivitis are as high as 99%. It...
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ndltd-TW-106NCTU54420582019-05-16T01:24:31Z http://ndltd.ncl.edu.tw/handle/9285ye A novel auto-fluorescence method for Dental plaque detection and classification 牙菌斑之新型自體螢光檢測與分類方法 Wang, Bo-Wen 王柏文 碩士 國立交通大學 電機工程學系 106 Dental health is a problem that modern people must pay attention to. According to the Health Promotion administration, the probability of getting dental caries is 88%, and those with periodontal disease such as periodontitis and gingivitis are as high as 99%. It is necessary to detect the presence of plaque in the mouth early. However, the design specifications of many products on the market are different, include the wavelength of the excitation light used. This study will use the hyperspectral microscopy system to observe the main bacteria of caries and periodontal disease and find out which has the highest response. In this research, we first improved the problems that may be encountered when observing bacteria with a microscope: the optical density of the filter and the weak fluorescence that may be caused by the material of the slide, and then the photobleaching of bacterial is discussed. Finding the difference between the spectra of each bacteria, using this difference to give a identify method, this method will have 99% sensitivity and 99% specificity, which can distinguish the target bacteria well. At the same time, we also use a hand-held device with a single band of excitation light and a filter for human oral imaging experiments to verify the effectiveness of our use of the band. Ou-Yang, Mang 歐陽盟 2018 學位論文 ; thesis 62 en_US |
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碩士 === 國立交通大學 === 電機工程學系 === 106 === Dental health is a problem that modern people must pay attention to. According to the Health Promotion administration, the probability of getting dental caries is 88%, and those with periodontal disease such as periodontitis and gingivitis are as high as 99%. It is necessary to detect the presence of plaque in the mouth early. However, the design specifications of many products on the market are different, include the wavelength of the excitation light used. This study will use the hyperspectral microscopy system to observe the main bacteria of caries and periodontal disease and find out which has the highest response. In this research, we first improved the problems that may be encountered when observing bacteria with a microscope: the optical density of the filter and the weak fluorescence that may be caused by the material of the slide, and then the photobleaching of bacterial is discussed. Finding the difference between the spectra of each bacteria, using this difference to give a identify method, this method will have 99% sensitivity and 99% specificity, which can distinguish the target bacteria well. At the same time, we also use a hand-held device with a single band of excitation light and a filter for human oral imaging experiments to verify the effectiveness of our use of the band.
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author2 |
Ou-Yang, Mang |
author_facet |
Ou-Yang, Mang Wang, Bo-Wen 王柏文 |
author |
Wang, Bo-Wen 王柏文 |
spellingShingle |
Wang, Bo-Wen 王柏文 A novel auto-fluorescence method for Dental plaque detection and classification |
author_sort |
Wang, Bo-Wen |
title |
A novel auto-fluorescence method for Dental plaque detection and classification |
title_short |
A novel auto-fluorescence method for Dental plaque detection and classification |
title_full |
A novel auto-fluorescence method for Dental plaque detection and classification |
title_fullStr |
A novel auto-fluorescence method for Dental plaque detection and classification |
title_full_unstemmed |
A novel auto-fluorescence method for Dental plaque detection and classification |
title_sort |
novel auto-fluorescence method for dental plaque detection and classification |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/9285ye |
work_keys_str_mv |
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