高污染基因芯片图像的网格划分
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摘要
对基因芯片图像进行网格划分是基因芯片图像处理的基础,针对高污染基因芯片图像中高亮污渍对网格划分造成的干扰,将高亮目标分为靶点、污渍块和污渍点3种类型,根据其不同特征分别进行处理,提出一种新的网格划分算法。利用全局分割确定高亮目标所在位置,根据污渍点的特征,使用图像腐蚀技术对其进行剔除;根据污渍块的特征,对其先进行膨胀处理,然后对其进行剔除,可消除污渍块及其边缘痕迹。使用自协方差对没有污渍的图像进行网格划分,实验表明,对于高污染基因芯片图像,该算法可以得到理想的网格划分结果,靶点检测平均准确率可以达到94.73%。
The gridding is a basic step in the gene chip image processing.To suppress the influence of the highlight stains which could interfere with the gridding of the high pollution gene chip images,the highlight objects were divided into three categories:spots,stain blocks and stain points.Based on its characteristics,every category was processed respectively and a new gridding algorithm was proposed.Firstly,the highlight objects were located by utilizing aglobal thresholding segmentation.Secondly,the strain spots were smoothed via image corrosion technology.The strain blocks were removed using a dilation technology to reduce the trace of edges.Finally,the autocovariance was utilized for gridding the noise-free gene chip images.Experimental results show that the proposed algorithm can obtain an ideal gridding result for the high pollution gene chip images and the spot detection average accuracy can reach 94.73%.
The gridding is a basic step in the gene chip image processing.To suppress the influence of the highlight stains which could interfere with the gridding of the high pollution gene chip images,the highlight objects were divided into three categories:spots,stain blocks and stain points.Based on its characteristics,every category was processed respectively and a new gridding algorithm was proposed.Firstly,the highlight objects were located by utilizing aglobal thresholding segmentation.Secondly,the strain spots were smoothed via image corrosion technology.The strain blocks were removed using a dilation technology to reduce the trace of edges.Finally,the autocovariance was utilized for gridding the noise-free gene chip images.Experimental results show that the proposed algorithm can obtain an ideal gridding result for the high pollution gene chip images and the spot detection average accuracy can reach 94.73%.
引文
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[3]SHAO Guifang,LI Gang,LI Tiejun,et al.Researchon noise processing algorithm for micro-array imagehighlighting[J].Journal of Xiamen University:Natu-ral Science Edition,2009(03):330-333.邵桂芳,李刚,李铁军,等.基因芯片图像高亮噪声处理算法研究[J].厦门大学学报:自然科学版,2009(03):330-333.
[4]LI Qingzhong,LIU Qing.Adaptive enhancement al-gorithm for low illumination images based on wavelettransform[J].Chinese Academy of lasers,2015(02):280-286.李庆忠,刘清.基于小波变换的低照度图像自适应增强算法[J].中国激光,2015(02):280-286.
[5]LI Bing,SU Juan,WANG Yanzhao.Infrared imageenhancement combined with PCNN segmentation andfuzzy set theory[J].Acta Optica Sinica,2016,(09):1-12.李冰,苏娟,王延钊.结合PCNN分割和模糊集理论的红外图像增强[J].光学学报,2016(09):1-12.
[6]YANG Jianqing,LUO Jijun,XU Jun,et al.Basedon the atmospheric environment shelter of the visibleimage enhancement of[J].Laser &OptoelectronicsProgress,2017,54(10):239-245.杨建清,罗积军,徐军,等.基于大气环境实验方舱的可见光图像增强研究[J].激光与光电子学进展,2017,54(10):239-245.
[7]MOENA D Q.Microarray image gridding by usingself-organizing maps[C].2011 5th International Con-ference on Bioinformatics and Biomedical Engineer-ing,2011:1-4.
[8]FOUAD A A,LABIB F Z M,SHARAWY A A R,et al.Developing a new methodology for de-noisingand gridding cDNA microarray images[C].US:IEEE,2012:142-145.
[9]LI Tiejun,SUN Yue,SHAO Guifang,et al.An ac-tive analysis method for microarray image gridding[J].Journal of Xiamen University:Natural Science,2016,55(6):888-894.李铁军,孙跃,邵桂芳,等.基于主动分析的基因芯片图像网格定位[J].厦门大学学报:自然版,2016,55(6):888-894.
[10]HARIKIRAN J,PHANENDRA M,SWAMY N N,et al.K-means with bi-dimensional empirical modedecomposition for segmentation of microarray image[J].InternationalJournal of Computer Science Is-sues,2012,9(5):427-433.
[11]DATTA A,KONG W K,YOW K C.A fully auto-matic method for gridding bright field images of bead-based microarrays[J].IEEE Journal of Biomedical &Health Informatics,2016,20(4):1148-1159.
[12]AHMAD M M,JAMBEK A B,MASHOR M Y B.A study on microarray image gridding techniques forDNA analysis[C].US:IEEE,2015:171-175.
[13]HELMY A K,EL-TAWEEL G S.Regular griddingand segmentation for microarray images[J].Comput-ers &Electrical Engineering, 2013, 39(7):2173-2182.
[14]BARIAMIS D,IAKOVIDIS D K,MAROULIS D.M3G:Maximum margin microarray gridding[J].Bmc Bioinformatics,2010,11(1):49.
[15]ZACHARIA E,MAROULIS D.An original geneticapproach to the fully automatic gridding of microar-ray images[J].IEEE Transactions on Medical Ima-ging,2008,27(6):805-13.
[16]RUEDA L,REZAEIAN I.A fully automatic grid-ding method for cDNA microarray images[J].BMCBioinformatics,2011,12(1):1-17.
[17]CHEN Z,TU Y.Improved image segmentation algo-rithm based on OTSU algorithm[J].InternationalJournal of Advancements in Computing Technology,2012,4(15):206-215.
[18]ZHANG Y,WU S.Statistsics-adaptive method forcDNA microarray images gridding[C].US:IEEE,2012:380-383.
[19]MUKHTAR M,JAMBEK A B,MASHOR M Y B.Image gridding algorithm for DNA microarray analy-ser[C].US:IEEE,2017:452-457.
[20]ZACHARIA E,MAROULIS D.A spot modelingevolutionary algorithm for segmenting microarray im-ages[M].Germany:InTech,2012.