Terahertz Imaging of Caries Wavelet Gradient Domain Reconstruction Fusion
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摘要: 龋病是发病率最高的口腔疾病，也是全世界最流行的疾病之一。太赫兹光谱成像技术因具有宽频带光谱分析能力强、空间分辨率高、电离辐射低等优势，有望成为龋病诊断的新技术手段。本文以含牙本质龋的牙齿切片为研究对象，进行反射式太赫兹光谱扫描，以频域幅值为参数对样品的光谱数据做二维重构成像，获得多幅不同频率下的龋齿太赫兹光谱图像。为了解决单一参数所得的龋齿太赫兹图像动态范围小、对比度低，边缘和细节位置模糊不清的问题，采用小波梯度域重建的融合方法，将多幅图像中梯度幅值较大的部分集中于一幅图像上，得到了一幅细节特征更加清晰完整的新图像。实验结果表明，融合后的图像相较于融合前的图像在信息熵、平均梯度以及对比度上均有所提高，不同组织之间的区分效果更加显著。Abstract: Caries is one of the most prevalent oral diseases worldwide. Terahertz spectral imaging technology has the advantages of strong broadband spectral analysis ability, high spatial resolution, and low ionizing radiation and is expected to be a new technical means for caries diagnosis. In this study, tooth slices containing dentin caries were used as the research object, and reflective terahertz spectral scanning was performed. The spectral data of the samples were reconstructed using two-dimensional imaging with the frequency-domain amplitude as the parameter, and several terahertz spectral images of caries were obtained at different frequencies. To solve the problems of small dynamic range, low contrast, and ambiguous edges, the detailed position of the terahertz image was obtained using a single parameter. Using the fusion method of wavelet gradient domain reconstruction, the larger gradient amplitudes of several images are concentrated in one image, and a new image with clearer and more complete detailed features is obtained. The experimental results show that the information entropy, average gradient, and contrast of the fused images are improved compared with those of the pre-fused images, and the discrimination effect between different tissues is more significant.
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Keywords:
- caries detection /
- terahertz image /
- gradient domain reconstruction /
- image fusion
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图 2 反射式太赫兹时域光谱成像系统原理示意图
Figure 2. Schematic diagram of the THz TDS system in the reflection mode
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图 3 牙本质、牙本质龋、牙釉质表面的平均太赫兹时域波形（左）及频谱（右）
Figure 3. Mean terahertz time domain waveform(left) and spectrum(right) of surfaces of dentin, caries and enamel
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图 4 01~03号样品反射式太赫兹光谱成像结果
Figure 4. Reflection terahertz spectral images of sample No.01-03
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图 5 Haar小波梯度域重建融合算法流程
Figure 5. Flow chart of Haar wavelet gradient domain reconstruction fusion algorithm
表 1 龋齿样品的融合前后太赫兹图像评价指标对比
Table 1 Terahertz image evaluation index comparison of caries samples before and after fusion
Sample Image IE AG CON 01 0.996THz 6.1947 0.0136 12.3335 1.244THz 6.5509 0.0215 27.3449 1.311THz 6.2496 0.0178 21.4012 Fusion image 7.0893 0.0266 35.4942 02 0.995THz 6.9376 0.0223 31.0891 1.147THz 7.1184 0.0249 36.5763 1.375THz 6.7764 0.0264 41.5308 Fusion image 7.4126 0.0319 50.7427 03 0.964THz 7.1886 0.0232 29.7532 1.030THz 7.1353 0.0206 24.6868 1.344THz 7.0889 0.0280 42.8916 Fusion image 7.6641 0.0399 73.7251 
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[1] 陈智, 卢展民, Falk Schwendicke, 等. 龋损管理: 龋坏组织去除的专家共识[J]. 中华口腔医学杂志, 2016, 51(12): 712-716. DOI: 10.3760/cma.j.issn.1002-0098.2016.12.003 CHEN Z, LU Z M, Falk S, et al. Managing carious lesions: consensus recommendations on carious tissue removal[J]. Chinese Journal of Stomatology, 2016, 51(12): 712-716. DOI: 10.3760/cma.j.issn.1002-0098.2016.12.003
[2] Maeng Youjin, Lee Hyungsuk, Lee Eunsong, et al. Noninvasive detection of microleakage in all-ceramic crowns using quantitative light-induced fluorescence technology[J]. Photodiagnosis and Photodynamic Therapy, 2020, 30: 101672. DOI: 10.1016/j.pdpdt.2020.101672
[3] Brouwer F, Askar H, Paris S, et al. Detecting secondary caries lesions: a systematic review and meta-analysis[J]. J. Dent Res. , 2016, 95(2): 143-151. DOI: 10.1177/0022034515611041
[4] FAN Shuting, HE Yuezhi, Benjamin S Ung, et al. The growth of biomedical terahertz research[J]. Journal of Physics D: Applied Physics, 2014, 47(37): 374009. DOI: 10.1088/0022-3727/47/37/374009
[5] 张卫方, 杜潮海, 刘濮鲲. 太赫兹技术在医学成像方面的应用和进展[J]. 标记免疫分析与临床, 2019, 26(9): 1603-1608. https://www.cnki.com.cn/Article/CJFDTOTAL-BJMY201909036.htm ZHANG W F, DU C H, LIU P K. The applications and developments of teraherz radiation in medical imaging[J]. Labeled Immunoassays and Clinical Medicine, 2019, 26(9): 1603-1608. https://www.cnki.com.cn/Article/CJFDTOTAL-BJMY201909036.htm
[6] 严芷瑶, 黄婉霞, 黄青青, 等. 太赫兹医学成像研究进展[J]. 光电工程, 2020, 47(5): 79-89. YAN Zhiyao, HUANG Wanxia, HUANG Qingqing, et al. Research progress of terahertz medical imaging[J]. Opto-Electronic Engineering, 2020, 47(5): 190721.
[7] Daniela S Lopes, Wellington S Souza, Renato E de Araujo, et al. Terahertz time-domain spectroscopy of healthy and carious dental tissues[C]//The European Conference on Lasers and Electro-Optics, 2019: 1-1(DOI: 10.1109/CLEOE-EQEC.2019.8871841).
[8] Karagoz B, Kamburoglu K, Altan H. Terahertz pulsed imaging for the monitoring of dental caries: a comparison with X-ray imaging[C]//Proceedings of SPIE, 2017, 10417P(https://doi.org/10.1117/12.2285991).
[9] Kamburoğlu Kıvanç, Karagöz Burcu, Altan Hakan, et al. An ex vivo comparative study of occlusal and proximal caries using terahertz and X-ray imaging[J]. Dento Maxillo Facial Radiology, 2019, 48(2): 20180250. DOI: 10.1259/dmfr.20180250
[10] Hampton P, Agathoklis P, Bradley C. A new wave-front reconstruction method for adaptive optics system using wavelets[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(5): 781-792. DOI: 10.1109/JSTSP.2008.2006386
[11] Sevcenco I S, Hampton P J, Agathoklis P. Seamless stitching of images based on a Haar wavelet 2D integration method[C]//17th International Conference on Digital Signal Processing (DSP), 2011: 1-6(DOI: 10.1109/ICDSP.2011.6004934).]
[12] Ioana S Sevcenco, Peter J Hampton, Panajotis Agathoklis. A wavelet based method for image reconstruction from gradient data with applications[J]. Multidimensional Systems and Signal Processing, 2015, 26(3): 717-737.
[13] YAN Qing, XU Yi, YANG Xiaokang, et al. Single image super resolution based on gradient profile sharpness[J]. IEEE Transactions on Image Processing, 2015, 24(10): 3187-3202. DOI: 10.1109/TIP.2015.2414877
[14] 杨艳春, 李娇, 王阳萍. 图像融合质量评价方法研究综述[J]. 计算机科学与探索, 2018, 12(7): 1021-1035. https://www.cnki.com.cn/Article/CJFDTOTAL-KXTS201807002.htm YANG Yanchun, LI Jiao, WANG Yangping. Review of image fusion quality evaluation methods[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(7): 1021-1035. https://www.cnki.com.cn/Article/CJFDTOTAL-KXTS201807002.htm
[15] 王跃华, 陶忠祥. 红外与可见光图像融合质量评价方法综述[J]. 红外, 2012, 33(6): 7-11. https://www.cnki.com.cn/Article/CJFDTOTAL-HWAI201206003.htm WANG Y, TAO Z. Overview of quality evaluation methods of fused infrared and visible images[J]. Infrared, 2012, 33(6): 7-10. https://www.cnki.com.cn/Article/CJFDTOTAL-HWAI201206003.htm
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