一种高通量dPCR荧光图像自适应增强算法
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摘要
目的为了改善荧光图像背景光照不均匀和对比度低的问题,提出一种荧光图像自适应亮度校正和低对比度增强算法。方法根据光照成像原理,利用引导滤波提取出荧光图像的光照分量,通过改进的二维Gamma函数动态校正背景光照,利用Top-hat变换分离出校正后的前景和背景,对前景进行自适应直方图均衡化,以实现荧光图像自适应增强的目的。结果对比传统算法,文中算法处理后的图像背景光照均匀,对比度增强效果明显,其中标准差平均提高了9.4倍,平均梯度平均提高了1.2倍,信息熵平均提高了0.2倍。结论文中算法可以改善高通量dPCR荧光图像背景光照不均匀性,提高图像对比度,突出图像中隐藏的细节,对其他荧光图像处理也具有参考价值。
The paper aims to propose an adaptive brightness correction and low contrast enhancement algorithm for fluorescence images to improve the background illumination unevenness and low contrast of fluorescent images. According to the principle of illumination imaging, the illumination component of the fluorescence image was extracted by guided filtering. The background illumination was dynamically corrected by the improved two-dimensional Gamma function. The corrected foreground and background were separated by Top-hat transformation. And the adaptive histogram equalization was performed on the foreground, to have adaptive enhancement of fluorescent images. Compared with the traditional algorithm, the background illumination of the image processed by the algorithm was uniform and the contrast enhancement effect was obvious. The standard deviation was improved by 9.4 times; the average gradient was increased by 1.2 times; and the information entropy was increased by 0.2 times. The algorithm can improve the background illumination unevenness of high-throughput dPCR fluorescence image, improve the image contrast, highlight hidden details in the image, and has reference value for processing of other fluorescence images.
The paper aims to propose an adaptive brightness correction and low contrast enhancement algorithm for fluorescence images to improve the background illumination unevenness and low contrast of fluorescent images. According to the principle of illumination imaging, the illumination component of the fluorescence image was extracted by guided filtering. The background illumination was dynamically corrected by the improved two-dimensional Gamma function. The corrected foreground and background were separated by Top-hat transformation. And the adaptive histogram equalization was performed on the foreground, to have adaptive enhancement of fluorescent images. Compared with the traditional algorithm, the background illumination of the image processed by the algorithm was uniform and the contrast enhancement effect was obvious. The standard deviation was improved by 9.4 times; the average gradient was increased by 1.2 times; and the information entropy was increased by 0.2 times. The algorithm can improve the background illumination unevenness of high-throughput dPCR fluorescence image, improve the image contrast, highlight hidden details in the image, and has reference value for processing of other fluorescence images.
引文
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[2]BISSELL M G.Digital PCR Analysis of Maternal Plasma for Noninvasive Detection of Sickle Cell Anemia[J].Yearbook of Pathology and Laboratory Medicine,2013:413-414.
[3]BEAVER J A,JELOVAC D,BALUKRISHNA S,et al.Detection of Cancer DNA in Plasma of Patients with Early-stage Breast Cancer[J].Clinical Cancer Research,2014,20(10):2643-2650.
[4]胡佳莹,姜羽,杨立桃.利用QuantStudio~(TM)3D数字PCR分析转基因玉米MON863含量[J].农业生物技术学报,2016,24(8):1216-1224.HU Jia-ying,JIANG Yu,YANG Li-tao.Analyzing MON863 Content in Transgenic Maize with QuantStudio~(TM)3 Digital PCR[J].Journal of Agricultural Biotechnology,2016,24(8):1216-1224.
[5]周淑芳,苟彤,方伟波,等.基于微流控芯片的数字PCR荧光图像分析方法[J].生命科学仪器,2018,16(1):27-31.ZHOU Shu-fang,GOU Tong,FANG Wei-bo,et al Digital PCR Fluorescence Image Analysis Method Based on Microfluidic Chip[J].Life Science,2018,16(1):27-31.
[6]MUKHTAR M,JAMBEK A B,MASHOR M Y B.Image Gridding Algorithm for DNA Microarray Analyser[C].International Conference on Electronic Design.IEEE,2016:452-457.
[7]ELTOUKHY M M.Segmentation of Complementary DNA Microarray Images using Markzr-Controlled Watershed Technique[J].International Journal of Computer Applications,2015,110(12):30-34.
[8]NASSER L,CORONADO P,DAVID E,et al.A Dictionary-Based Approach to Reduce Noise in Fluorescent Microscopy Images[J].IEEE International Conference on Signal and Image Processing(ICSIP),Singapore,2017:144-148.
[9]BAI X,ZHOU F,XUE B.Infrared Image Enhancement Through Contrast Enhancement by Using Multiscale New Top-Hat Transform[J].Infrared Physics&Technology,2011,54(2):61-69.
[10]丁红军,龚莹岚,邢克礼.基于MATLAB的低密度基因芯片图像处理[J].生物医学工程与临床2006(S1):39.DING Hong-jun,GONG Ying-lan,XING Ke-li.Image Processing of Low Density Gene Chip Based on MATLAB[J].Biomedical Engineering and Clinical Medicine,2006(S1):39.
[11]李铁军,孙跃,邵桂芳.一种基因芯片图像的对比度增强方法[J].厦门理工学院学报,2015(5):29-34.LI Tie-jun,SUN Yue,SHAO Gui-fang.A Contrast Enhancement Method for Gene Chip Images[J].Journal of Xiamen University of Technology,2015(5):29-34.
[12]常敏,刘天成.基于荧光显微镜的显微视觉图像增强处理[J].电子科技,2017,30(9):108-111.CHANG Min,LIU Tian-cheng.Microscopic Visual Image Enhancement Processing Based on Fluorescence Microscopy[J].Electronic Science&Technology2017,30(9):108-111.
[13]HE K,SUN J,TANG X.Guided Image Filtering[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2013,35(6):1397-1409.
[14]LEE S.An Efficient Content-Based Image Enhancement in the Compressed Domain Using Retinex Theory[J].IEEE Transactions on Circuits and Systems for Video Technology,2007,17(2):199-213.
[15]章毓晋.图像处理[M].北京:清华大学出版社,2012.ZHANG Yu-jin.Picture Processing[M].Beijing:Tsinghua University Press,2012
[16]STARK J A.Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equalization[J].IEEE Transactions on Image Processing,2000,9(5):889-896.
[17]PERREAULT S,HEBERT P.Median Filtering in Constant Time[J].IEEE Transactions on Image Processing2007,16(9):2389-2394.
[18]ELLIS D M,DRAPER N P,SMITH H S.Applied Regression Analysis[J].Technometrics,1999,9(1):182-183.
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