Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Reliable and robust quantification of signal intensities is a critical step in microarray-based biomedical studies. Hover, traditional techniques for microarray image processing would face significant challenges if the number of pixels used for the quantification of the local background and the foreground decreases dramatically.  have developed a new method, ELB-Q, which, by design, is ll suited for the image quantification of microarrays with very high density of spot layout (large number of spots arranged in unit area). In ELB-Q, a large extended local background (ELB) inter spot region excluding those ldquonoise of the backgroundrdquo pixels is used for estimating the local background, and the quantification of spot intensities (mean and median) in the putative target spot regions is performed after further excluding background pixels in these areas based on the cutoff values established during the ELB calculation. ELB-Q takes advantage of the abundant spatial information around each spot of interest, makes no assumption of the shape and size of the spots, and needs no sophisticated adjustment.  show results of image processing using ELB-Q on both the simulated data and real DNA microarrays, which compare favorably in robustness and accuracy against those obtained with Gene Pix Pro 6.0 (Axon Instruments, 1999) and the Markov random field (MRF) modeling approach (O. Demirkaya , Bioinformatics, vol. 21, pp. 2994-3000, 2005). The ELB-Q software is developed in Matlab, and is available upon request.
['Marc Q. Ma', 'Kai Zhang', 'Hui Yun Wang', 'Frank Y. Shih']
I am writing a research paper on a idea ELB-Q: A New Method for Improving the Robustness in DNA Microarray Image Quantification
[{"from": "human", "value": "I am writing a research paper on a idea ELB-Q: A New Method for Improving the Robustness in DNA Microarray Image Quantification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Reliable and robust quantification of signal intensities is a critical step in microarray-based biomedical studies. Hover, traditional techniques for microarray image processing would face significant challenges if the number of pixels used for the quantification of the local background and the foreground decreases dramatically.  have developed a new method, ELB-Q, which, by design, is ll suited for the image quantification of microarrays with very high density of spot layout (large number of spots arranged in unit area). In ELB-Q, a large extended local background (ELB) inter spot region excluding those ldquonoise of the backgroundrdquo pixels is used for estimating the local background, and the quantification of spot intensities (mean and median) in the putative target spot regions is performed after further excluding background pixels in these areas based on the cutoff values established during the ELB calculation. ELB-Q takes advantage of the abundant spatial information around each spot of interest, makes no assumption of the shape and size of the spots, and needs no sophisticated adjustment.  show results of image processing using ELB-Q on both the simulated data and real DNA microarrays, which compare favorably in robustness and accuracy against those obtained with Gene Pix Pro 6.0 (Axon Instruments, 1999) and the Markov random field (MRF) modeling approach (O. Demirkaya , Bioinformatics, vol. 21, pp. 2994-3000, 2005). The ELB-Q software is developed in Matlab, and is available upon request."}]