Background correction method for DNA microarray image processing

Most microarray image scanning approaches provide an estimation of the intensity of the foreground and background for each spot. Background intensity must be corrected in order to remove the effect of non-specific binding or spatial heterogeneity across the array, but when such corrections are applied many problems appear, such as negative intensity for the spot or high variability of low-intensity log ratios. In this paper, many alternative methods for calculating background intensity are discussed and many approaches for background correction are tested and compared. GenePix, ScanAlyze and QuantArry are the strategies that were reviewed for background locations to extract their intensity. Similarly, to GenePix, a new approach for background calculation was proposed and tested. It shows more accurate results and the occurrences of error become lesser.

A Fully Automated Method for Noisy cDNA Microarray Image Quantification

DNA microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. Image processing is an important aspect of microarray experiments, the primary purpose of the image analysis step is to extract numerical foreground and background intensities for the red and green channels for each spot on the microarray. The background intensities are used to correct the foreground intensities for local variation on the array surface, resulting in corrected red and green intensities for each spot that can be considered as a primary data for subsequent analysis. Most techniques divide the overall microarray image processing into three steps: gridding, segmentation, and quantification. In this paper, a  simple automated gridding technique is developed with a great effect on noisy microarray images. A segmentation technique based on ‘edge-detection’ is applied to identify the spots and separate the foreground from the background is known as microarray image segmentation. Finally, a quantification technique is used to calculate the gene expression level from the intensity values of the red and green components of the image. Results revealed that the developed methods can deal with various kinds of noisy microarray images, with high  griddingaccuracy of 92.2% for low quality images and 100% for high quality images resulting in better spot quantification to get more accurate gene expression values.Â

AUTOMATED MICROARRAY IMAGE GRIDDING USING IMAGE PROJECTION VECTORS COUPLED WITH POWER SPECTRUM MODEL

Microarray technology has been increasingly recognized as a powerful means for monitoring the expression levels of thousands of genes simultaneously. Microarray image processing is an essential aspect of microarray experiment, of which gridding is thought to be the most important step of spot recognition. Many times, microarray image gridding requires assisted intervention to achieve the acceptable accuracy. In this paper, an automatic microarray image gridding algorithm was presented by using image projection vectors together with power spectrum model. For obtaining grid position, the image projection vectors were utilized by adequately considering the grid parameters. On the other hand, as a preprocessing procedure of microarray gridding, detection of the grid rotation was involved in our study by using power spectrum analyses of the image projection vectors. Our approach has been evaluated by three different microarray datasets. Experimental comparisons with up-to-date approaches by using both synthetic and real image data are demonstrated. The gridding result was shown to be very accurate, and able to provide correct gridding dataset for the downstream microarray analyses. In summary, our study demonstrated the combination of image projection vectors with power spectrum model as a powerful strategy for microarray image gridding.