A Fully Automated Method for Noisy cDNA Microarray Image Quantification

Abstract

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

Automatic Segmentation of cDNA Microarray Images Using Different Methods

“…To avoid that possibility, it is safer to fill each spot; take each bounding box region at a time by looping. Indeed, the spot segmentation now looks quite good [14]. …”