采用最小误差阈值分割算法的基因芯片图像分析
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摘要
为了能够较好地处理芯片图像,尽可能准确地提取出描述基因样点的数据信息,采用了最小误差阈值的分割算法.该方法在假设目标和背景的分布服从混合正态分布的前提下,设定了最小误差分类目标函数,通过求得使目标函数值最小的最佳分割阈值,实现基因样点和背景图像的分割.针对分割出来的基因样点图像提取特征数据,最后对这些数据进行聚类分析,进而对实验样点进行分类.在实验中应用该方法分析了2组基因芯片图像,基因样点的分类效果较好,验证了该基因芯片分析方法的可行性.
In order to analyze gene chip image better,along with extract the data information as accurately as possible,to describe the gene sample,this research paper proposes to implement a minimum error threshold segmentation method.Based on the assumption that the distributions of object and background are governed by a mixture normal distribution,this method sets an objective function of minimum error classification.This method also allows for the implementation of the segmentation between gene sample and background image through calculating the optimal segmentation threshold by minimizing the objective function.Next,the feature data from the segment of gene sample image was extracted and a clustering analysis with the data was done to realize the successful classification of the experimental samples.The study examined two groups of gene chip images and analyzed them by using this method in the experiment.The results show that the classification result was better and the feasibility of the analysis method was verified.
In order to analyze gene chip image better,along with extract the data information as accurately as possible,to describe the gene sample,this research paper proposes to implement a minimum error threshold segmentation method.Based on the assumption that the distributions of object and background are governed by a mixture normal distribution,this method sets an objective function of minimum error classification.This method also allows for the implementation of the segmentation between gene sample and background image through calculating the optimal segmentation threshold by minimizing the objective function.Next,the feature data from the segment of gene sample image was extracted and a clustering analysis with the data was done to realize the successful classification of the experimental samples.The study examined two groups of gene chip images and analyzed them by using this method in the experiment.The results show that the classification result was better and the feasibility of the analysis method was verified.
引文
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[11]王晓凯,李峰.改进的自适应中值滤波[J].计算机工程与应用,2010,46(3):175-176,218.WANG Xiaokai,LI Feng.Improved adaptive median filte-ring[J].Computer Engineering and Applications,2010,46(3):175-176,218.
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[13]胡园园,孙啸,何农跃,等.基于图像投影的基因芯片图像网格定位[J].生物医学工程学杂志,2005,22(4):668-671.HU Yuanyuan,SUN Xiao,HE Nongyue,et al.A gene-chip image grid localization method based on profiles of im-age[J].Journal of Biomedical Engineering,2005,22(4):668-671.
[14]李明华,刘全,刘忠,等.数据挖掘中聚类算法的新发展[J].计算机应用研究,2008,25(1):13-17.LI Minghua,LIU Quan,LIU Zhong,et al.New develop-ments of clustering methods in data mining[J].Applica-tion Research of Computers,2008,25(1):13-17.
[2]WIESE K C,EICHER C.Graph drawing tools for bioinfor-matics research:an overview computer based medical sys-tems[C]//Proceedings of the19th IEEE Symposium on Computer-Based Medical Systems.Washington,DC,USA:IEEE Computer Society,2006:653-658.
[3]张晶,王黎,高晓蓉,等.数字图像处理中的图像分割技术及其应用[J].信息技术,2010(11):33-39.ZHANG Jing,WANG Li,GAO Xiaorong,et al.The image segmentation technology and its application in digital image processing[J].Information Technology,2010(11):33-39.
[4]CECCARELLI M,ANTONIOL G.A deformable grid-matc-hing approach for microarray images[J].IEEE Transactions on Image Processing,2006,15(10):3178-3188.
[5]BAJCSY P.An overview of DNA microarray grid alignment and foreground separation approaches[J].EURASIP Jour-nal on Applied Signal Processing,2006,2006(1):080163.
[6]王宇,陈殿仁,沈美丽,等.基于形态学梯度重构和标记提取的分水岭图像分割[J].中国图象图形学报,2008,13(11):2176-2180.WANG Yu,CHEN Dianren,SHEN Meili,et al.Watershed segmentation based on morphological gradient reconstruction and marker extraction[J].Journal of Image and Graphics,2008,13(11):2176-2180.
[7]刘华军,任明武,杨静宇.一种改进的基于模糊聚类的图像分割方法[J].中国图象图形学报,2006,11(9):1312-1316.LIU Huajun,REN Mingwu,YANG Jingyu.An improved image segmentation method based on fuzzy clustering[J].Journal of Image and Graphics,2006,11(9):1312-1316.
[8]范九伦,雷博.二维直线型最小误差阈值分割法[J].光电工程,2009,31(8):1801-1806.FAN Jiulun,LEI Bo.Two-dimensional linear-type minimum error threshold segmentation method[J].Journal of Elec-tronics&Information Technology,2009,31(8):1801-1806.
[9]谭优,王泽勇.图像阈值分割算法实用技术研究与比较[J].计算机信息,2007,23(24):233,298-299.TAN You,WANG Zeyong.Study on applied technology a-rithmetic of image threshold segmentation[J].Microcom-puter Information,2007,23(24):233,298-299.
[10]凡敏,田明尧,赵权,等.基孔肯亚病毒和辛德毕斯病毒检测基因芯片的建立[J].中国兽医学报,2012,32(10):1493-1497.FAN Min,TIAN Mingrao,ZHAO Quan,et al.Establish-ment and application of gene chip for Chikungunya virus and Sindbis virus[J].Chinese Journal of Veterinary Sci-ence,2012,32(10):1493-1497.
[11]王晓凯,李峰.改进的自适应中值滤波[J].计算机工程与应用,2010,46(3):175-176,218.WANG Xiaokai,LI Feng.Improved adaptive median filte-ring[J].Computer Engineering and Applications,2010,46(3):175-176,218.
[12]郭海霞,谢凯.一种改进的自适应中值滤波算法[J].中国图象图形学报,2007,12(7):1185-1188.GUO Haixia,XIE Kai.An improved method of adaptive median filter[J].Journal of Image and Graphics,2007,12(7):1185-1188.
[13]胡园园,孙啸,何农跃,等.基于图像投影的基因芯片图像网格定位[J].生物医学工程学杂志,2005,22(4):668-671.HU Yuanyuan,SUN Xiao,HE Nongyue,et al.A gene-chip image grid localization method based on profiles of im-age[J].Journal of Biomedical Engineering,2005,22(4):668-671.
[14]李明华,刘全,刘忠,等.数据挖掘中聚类算法的新发展[J].计算机应用研究,2008,25(1):13-17.LI Minghua,LIU Quan,LIU Zhong,et al.New develop-ments of clustering methods in data mining[J].Applica-tion Research of Computers,2008,25(1):13-17.