Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images

CORC > 厦门大学 > 信息技术－已发表论文

	Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images
	Shao, Gui-Fang ; Yang, Fan ; Zhang, Qian ; Zhou, Qi-Feng ; Luo, Lin-Kai ; Shao GF(邵桂芳) ; Yang F(杨帆) ; Zhou QF(周绮凤) ; Luo LK(罗林开)
刊名	http://dx.doi.org/10.1109/TCBB.2012.130
	2013
关键词	SEGMENTATION
英文摘要	National Defense Basic Scientific Research program of China [B1420110155]; National Natural Science Foundations of China under Natural Science Foundation of Fujian province [61202144, 2009J05153]; Fundamental Research Funds for the Central Universities [2010121065]; Foundation of Key Laboratory of System Control and Information Processing; Ministry of Education, P.R. China [SCIP2011004]; Gridding is the first and most important step to separate the spots into distinct areas in microarray image analysis. Human intervention is necessary for most gridding methods, even if some so-called fully automatic approaches also need preset parameters. The applicability of these methods is limited in certain domains and will cause variations in the gene expression results. In addition, improper gridding, which is influenced by both the misalignment and high noise level, will affect the high throughput analysis. In this paper, we have presented a fully automatic gridding technique to break through the limitation of traditional mathematical morphology gridding methods. First, a preprocessing algorithm was applied for noise reduction. Subsequently, the optimal threshold was gained by using the improved Otsu method to actually locate each spot. In order to diminish the error, the original gridding result was optimized according to the heuristic techniques by estimating the distribution of the spots. Intensive experiments on six different data sets indicate that our method is superior to the traditional morphology one and is robust in the presence of noise. More importantly, the algorithm involved in our method is simple. Furthermore, human intervention and parameters presetting are unnecessary when the algorithm is applied in different types of microarray images.
语种	英语
出版者	IEEE COMPUTER SOC
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92519]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Shao, Gui-Fang,Yang, Fan,Zhang, Qian,et al. Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images[J]. http://dx.doi.org/10.1109/TCBB.2012.130,2013.
APA	Shao, Gui-Fang.,Yang, Fan.,Zhang, Qian.,Zhou, Qi-Feng.,Luo, Lin-Kai.,...&罗林开.(2013).Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images.http://dx.doi.org/10.1109/TCBB.2012.130.
MLA	Shao, Gui-Fang,et al."Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images".http://dx.doi.org/10.1109/TCBB.2012.130 (2013).