Issue No. 01 - Jan.-Feb. (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.130
Gui-Fang Shao , Dept. of Autom., Xiamen Univ., Xiamen, China
Fan Yang , Dept. of Autom., Xiamen Univ., Xiamen, China
Qian Zhang , Dept. of Autom., Xiamen Univ., Xiamen, China
Qi-Feng Zhou , Dept. of Autom., Xiamen Univ., Xiamen, China
Lin-Kai Luo , Dept. of Autom., Xiamen Univ., Xiamen, China
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.
Noise, Morphology, Fluorescence, Image segmentation, Information filters, Feature extraction
Gui-Fang Shao, Fan Yang, Qian Zhang, Qi-Feng Zhou and Lin-Kai Luo, "Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 1, pp. 181-192, 2013.