|
|
|
|
|
|
Research of Dental Caries Lesion Based on the Visible-Near Infrared Spectrum Polarization Detection |
LIANG Tian-quan1, DUAN Xiao-jie2, TANG Qing-xin1, YU Quan-zhou1, ZHANG Bao-hua1 |
1. School of Environment and Planning, Liaocheng University, Liaocheng 252059, China
2. Department of Stomatology, Liaocheng People’s Hospital, Liaocheng 252000, China |
|
|
Abstract In view of the problem to effectively characterize the damage of dental caries, we explore a kind of polarization spectrum detection method which have nondestructive and low consumption characteristics, it’s a beneficial to supplement for the conventional detected methods such as chemical analysis, surface profilometry and microradiography. Based on the characteristic which polarization spectral sensitive to the observation sample surface microstructure, dental caries lesion due to the demineralization process, as result calcium and phosphate are dissolved from the enamel and dentin. The tooth surface microstructure has taken place different change; the structure present different degree of difference in light scattering properties and phase change. Considering different dental caries samples its surface microstructure changes strongly associated with the polarization information, we put forward a kind of method which can effectively characterize different teeth from caries by using polarization spectrum. Selected the 450, 550, 670 and 860 nm as four different observation research wavelengths, as well as selected six different dental caries samples, the degree of polarization as the parameter to describe different samples polarization characteristics. The experimental results show that the consistent observation waveband for different tooth samples is positively related to the degree of polarization parameters, as well as the same observation samples showed observation waveband is negatively related to the polarization characteristics. For further quantitative characterization, the relationship between polarization spectrum and tooth decay damage levels, the index correlation mathematical model which can interpret internal coupling was built. To effectively validate model robustness, it needed quantitative validation the model simulation results and measured data. We selected the quantitative evaluation factor such as the sum of squares due to error (SSE), root means squared error (RMSE), coefficient of determination (R-square). The results show that models R-square close to 1, as well as the SSE and RMSE values are small. The model characteristic of robustness and effectiveness was verified, which can effectively interpret different caries damage teeth samples from polarization coupling relation. This research is effectively extending the teeth caries detection method, as well as revealed polarization spectrum can be effective characterization and distinction to different dental caries samples. It is also developing a new kind of nondestructive and low-cost polarization spectrum detection technology.
|
Received: 2019-12-08
Accepted: 2020-04-06
|
|
|
[1] Sheiham A, James W P T. Journal of Dental Research, 2015, 94(10): 1341.
[2] Christelle A N, Fabrice P, Hadi L, et al. Journal of Biomedical Optics, 2016, 21(7): 071103-1.
[3] Frencken J E, Sharma P, Stenhouse L, et al. Journal of Clinical Periodontology, 2017, 44(S18): S94.
[4] Pu Yuanyuan, Feng Yaoze, Sun Dawen. Comprehensive Reviews in Food Science and Food Safety, 2015, 14(2): 176.
[5] WANG Shuang, Zeng Hai-shan(王 爽, Zeng Haishan). Chinese Journal of Lasers(中国激光), 2018, 45(2): 0207002-1.
[6] Orgiazzi A, Ballabio C, Panagos P, et al. European Journal of Soil Science, 2018, 69: 140.
[7] LI Su-wen, MOU Fu-sheng, HU Li-sha, et al(李素文, 牟福生, 胡丽莎, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(11): 3403.
[8] Song Kaishan, Li Sijia, Wen Zhidan, et al. Journal of Hydrology, 2019, 579: 1.
[9] Xie Qiaolin, Zeng Nan, Huang Yu, et al. Biomedical Optical Express, 2019, 10(7): 3269.
[10] Liang Tianquan, Sun Xiaobing, Wang Han, et al. Journal of Sensors, 2016, 3569272: 1.
[11] He C, Chang J T, Hu Q, et al. Nat. Commun., 2019, 10(1):4264. doi: 10.1038/s41467-019-12286-3.
[12] Rubin N A, Aversa G D, Chevalier P, et al. Science, 2019, 365(6448): 1. |
[1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
[2] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[3] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
[4] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[5] |
HAO Zi-yuan1, YANG Wei1*, LI Hao1, YU Hao1, LI Min-zan1, 2. Study on Prediction Models for Leaf Area Index of Multiple Crops Based on Multi-Source Information and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3862-3870. |
[6] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
[7] |
LIANG Ya-quan1, PENG Wu-di1, LIU Qi1, LIU Qiang2, CHEN Li1, CHEN Zhi-li1*. Analysis of Acetonitrile Pool Fire Combustion Field and Quantitative
Inversion Study of Its Characteristic Product Concentrations[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3690-3699. |
[8] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[9] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
LIU Bo-yang1, GAO An-ping1*, YANG Jian1, GAO Yong-liang1, BAI Peng1, Teri-gele1, MA Li-jun1, ZHAO San-jun1, LI Xue-jing1, ZHANG Hui-ping1, KANG Jun-wei1, LI Hui1, WANG Hui1, YANG Si2, LI Chen-xi2, LIU Rong2. Research on Non-Targeted Abnormal Milk Identification Method Based on Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3009-3014. |
[12] |
WANG Peng1, GAO Yong-bao1*, KOU Shao-lei1, MEN Qian-ni1, ZHANG Min1, HE Tao1, YAO Wei2, GAO Rui1, GUO Wen-di1, LIU Chang-rui1. Multi-Objective Optimization of AAS Conditions for Determination of Gold Element Based on Gray Correlation Degree-RSM Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3117-3124. |
[13] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
[14] |
LIU Wen-bo, LIU Jin, HAN Tong-shuai*, GE Qing, LIU Rong. Simulation of the Effect of Dermal Thickness on Non-Invasive Blood Glucose Measurement by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2699-2704. |
[15] |
ZHANG Jun-he, YU Hai-ye, DANG Jing-min*. Research on Inversion Model of Wheat Polysaccharide Under High Temperature and Ultraviolet Stress Based on Dual-Spectral Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2705-2709. |
|
|
|
|