Method of High Precision Medical Infrared Thermography
-
摘要: 医学红外热像设备测得的红外数据及转换得到的温度数据难以直接判定其所属的人体区域，常需将其转为图像数据，利用图像处理技术得到感兴趣区域并从区域内温度数据得到生物特征，实现疾病的筛查或诊断。然而，从14位红外数据转换到8位图像数据存在严重的数据精度损失，导致处理性能欠佳。本文提出一种新的热像图表达方法，所得到的彩色热像图含原精度的温度数据信息，且含温度观察窗设定尺度下的彩色增强效果，同时载有温度数据记录和观察窗设定规则，通过对图像数据的逆变换，可以再现原始温度数据，并可改变彩色增强效果。该方法提供的热像图无需额外存取温度数据文件，在不同的红外热像系统间具有通用性，将更符合大数据和人工智能的发展趋势。Abstract: It is difficult to directly distinguish the human body area identified by the infrared data measured by medical infrared thermal imaging equipment from the temperature data obtained by conversion. It is often necessary to convert it into image data and use image processing technology to obtain the region of interest and the biological characteristics from the temperature data in the given area. Accordingly, disease screening or diagnosis can be realized. However, conversion from 14-bit infrared data to 8-bit image data incurs a serious loss of data accuracy, resulting in poor processing performance. In this paper, a new expression method for thermal images is proposed. The obtained color thermal image contains the original precision temperature data information and the color enhancement effect under the setting scale of the temperature observation window. At the same time, it contains the temperature data record and the setting rules of the observation window. Through the inverse transformation of the image data, the original temperature data can be reproduced and the color enhancement effect can be changed. The thermal image provided by this method can be used in different infrared thermal image systems without requiring additional access to temperature data files. This will be more aligned with the development trend of big data and artificial intelligence.
-
图 1 红外热像机输出的原灰度图和伪彩色图
Figure 1. Original gray scale and pseudo color image output by infrared thermal imager
图 2 新方法得到的红外热图像及其色带
Figure 2. Infrared thermal image and its color band obtained by new method
图 3 对新的红外热像图通过温度观察窗进行增强
Figure 3. Enhance the new infrared thermal image through the temperature observation window
图 5 原灰度图像和新灰度图像的边缘检测对比
Figure 5. Comparison of edge detection between original gray image and new gray image
图 6 新图像去除背景信息的效果图
Figure 6. Effect picture of removing background information from new image
图 7 不同RGB排列方式得到的新的红外热像图的效果图
Figure 7. Effect picture of new infrared thermal image obtained by different RGB arrangement
-
[1] Bhowmik M K, Gogoi U R, Majumdar G, et al. Designing of Ground-Truth-Annotated DBT-TU-JU Breast Thermogram Database Toward Early Abnormality Prediction[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22: 1238-1249. doi: 10.1109/JBHI.2017.2740500 [2] De Santana M A, Pereira J M S, Da Silva F L, et al. Breast cancer diagnosis based on mammary thermography and extreme learning machines[J]. Research on Biomedical Engineering, 2018, 34: 45-53. doi: 10.1590/2446-4740.05217 [3] Dua G, Mulaveesala R. Applicability of active infrared thermography for screening of human breast: a numerical study[J]. Journal of Biomedical Optics, 2018, 23: 9. https://www.ncbi.nlm.nih.gov/pubmed/29560626 [4] Mambou S J, Maresova P, Krejcar O, et al. Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model[J]. Sensors, 2018, 18: 19. [5] Morales-Cervantes A, Kolosovas-Machuca E S, Guevara E, et al. an Automated Method for the Evaluation of Breast Cancer Using Infrared Thermography[J]. Excli Journal, 2018, 17: 989-998. https://www.ncbi.nlm.nih.gov/pubmed/30564079 [6] Santana M A d, Pereira J M S, Silva F L d, et al. Breast cancer diagnosis based on mammary thermography and extreme learning machines[J]. Research on Biomedical Engineering, 2018, 34: 45-53. doi: 10.1590/2446-4740.05217 [7] Wahab A A, Salim M I M, Yunus J, et al. Comparative evaluation of medical thermal image enhancement techniques for breast cancer detection[J]. Journal of Engineering and Technological Sciences, 2018, 50: 40-52. doi: 10.5614/j.eng.technol.sci.2018.50.1.3 [8] Abdel-Nasser M, Moreno A, Puig D. Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods[J]. Electronics, 2019, 8: 18. http://www.researchgate.net/publication/330412196_Breast_Cancer_Detection_in_Thermal_Infrared_Images_Using_Representation_Learning_and_Texture_Analysis_Methods [9] Singh D, Singh A K. Role of image thermography in early breast cancer detection- Past, present and future[J]. Computer Methods and Programs in Biomedicine, 2020, 183: 61-69. https://www.sciencedirect.com/science/article/pii/S0169260719311277 [10] Fokam D, Lehmann C. Clinical assessment of arthritic knee pain by infrared thermography[J]. Journal of basic and clinical physiology and pharmacology, 2018, 30: 21-25. http://www.ncbi.nlm.nih.gov/pubmed/30375348 [11] Pauk J, Wasilewska A, Ihnatouski M. Infrared thermography sensor for disease activity detection in Rheumatoid arthritis patients[J]. Sensors (Switzerland), 2019, 19: 34-48. https://www.mdpi.com/1424-8220/19/16/3444/pdf [12] Pauk J, Ihnatouski M, Wasilewska A. Detection of inflammation from finger temperature profile in rheumatoid arthritis[J]. Medical & Biological Engineering & Computing, 2019, 57: 2629-2639. doi: 10.1007/s11517-019-02055-1.pdf [13] Gatt A, Mercieca C, Borg A, et al. A comparison of thermographic characteristics of the hands and wrists of rheumatoid arthritis patients and healthy controls[J]. Scientific Reports, 2019, 9: 172-180. doi: 10.1038/s41598-018-36890-3 [14] Haq T, Crane J D, Kanji S, et al. Optimizing the methodology for measuring supraclavicular skin temperature using infrared thermography; implications for measuring brown adipose tissue activity in humans[J]. Scientific Reports, 2017, 7: 9. doi: 10.1038/s41598-017-00053-7 [15] Jimenez-Pavon D, Corral-Perez J, Sanchez-Infantes D, et al. Infrared Thermography for Estimating Supraclavicular Skin Temperature and BAT Activity in Humans: A Systematic Review[J]. Obesity, 2019, 27: 1932-1949. doi: 10.1002/oby.22635 [16] LIN P H, Echeverria A, Poi M J. Infrared thermography in the diagnosis and management of vasculitis[J]. Journal of vascular surgery cases and innovative techniques, 2017, 3: 112-114. doi: 10.1016/j.jvscit.2016.12.002 [17] Gauci J, Falzon O, Formosa C, et al. Automated Region Extraction from Thermal Images for Peripheral Vascular Disease Monitoring[J]. Journal of Healthcare Engineering, 2018, 2018: 14. https://www.hindawi.com/journals/jhe/2018/5092064/ [18] Carriere M E, de Haas L E M, Pijpe A, et al. Validity of thermography for measuring burn wound healing potential[J]. Wound Repair and Regeneration, 2019, 10: 1-8. https://pubmed.ncbi.nlm.nih.gov/31777128/ [19] Knobel-Dail R B, Holditch-Davis D, Sloane R, et al. Body temperature in premature infants during the first week of life: Exploration using infrared thermal imaging[J]. Journal of Thermal Biology, 2017, 69: 118-123. doi: 10.1016/j.jtherbio.2017.06.005 [20] Topalidou A, Ali N, Sekulic S, et al. Thermal imaging applications in neonatal care: a scoping review[J]. Bmc Pregnancy and Childbirth, 2019, 19: 14. doi: 10.1186/s12884-018-2132-3 [21] Pereira T, Nogueira-Silva C, Simoes R. Normal range and lateral symmetry in the skin temperature profile of pregnant women[J]. Infrared Physics & Technology, 2016, 78: 84-91. https://www.sciencedirect.com/science/article/pii/S1350449516302067 [22] Martini G, Cappella M, Culpo R, et al. Infrared thermography in children: a reliable tool for differential diagnosis of peripheral microvascular dysfunction and Raynaud's phenomenon?[J]. Pediatric Rheumatology, 2019, 17: 9. doi: 10.1186/s12969-019-0307-8 [23] Garcia-Porta N, Gantes-Nunez F J, Tabernero J, et al. Characterization of the ocular surface temperature dynamics in glaucoma subjects using long-wave infrared thermal imaging[J]. Journal of the Optical Society of America a-Optics Image Science and Vision, 2019, 36: 1015-1021. doi: 10.1364/JOSAA.36.001015 [24] Debiec-Bak A, Wojtowicz D, Pawik L, et al. Analysis of body surface temperatures in people with Down syndrome after general rehabilitation exercise[J]. Journal of Thermal Analysis and Calorimetry, 2019, 135: 2399-2410. doi: 10.1007/s10973-018-7345-1 [25] Hernandez-Contreras D A, Peregrina-Barreto H, Rangel-Magdaleno J D, et al. Plantar Thermogram Database for the Study of Diabetic Foot Complications[J]. IEEE Access, 2019, 7: 161296-161307. doi: 10.1109/ACCESS.2019.2951356 [26] 丁德红. 16位高精度在线式红外热像仪的技术方案与实现[J].红外技术, 2017, 39(9): 841-847. http://www.cnki.com.cn/Article/CJFDTOTAL-HWJS201709011.htmDehong D. Online Temperature Measurement Technology Solutions and Implementationof 16 bit Infrared Thermal Imager[J]. Infrared Technology, 2017, 39(9): 841-847. http://www.cnki.com.cn/Article/CJFDTOTAL-HWJS201709011.htm [27] Tan J H, Acharya U R. Pseudocolours for thermography-Multi-segments colour scale[J]. Infrared Physics & Technology, 2015, 72: 140-147. https://www.sciencedirect.com/science/article/pii/S1350449515001875 [28] Kermani S, Samadzadehaghdam N, EtehadTavakol M. Automatic color segmentation of breast infrared images using a Gaussian mixture model[J]. Optik, 2015, 126: 3288-3294. doi: 10.1016/j.ijleo.2015.08.007 [29] LI T J, WANG Y Y, CHANG C, et al. Color-appearance-model based fusion of gray and pseudo-color images for medical applications[J]. Information Fusion, 2014, 19: 103-114. doi: 10.1016/j.inffus.2012.07.002 -
下载:
点击查看大图
图(8)
计量
- 文章访问数: 781
- HTML全文浏览量: 423
- PDF下载量: 99
- 被引次数: 0
