American Journal of Biomedical Engineering 2012, 2(3): 115-119 DOI: 10.5923/j.ajbe.20120203.04 An Efficient Fully Automated Method for Gridding Microarray Images Fatma El-Zahraa Labib 1 , Islam Fouad 2 , Mai Mabrouk 3,* , Amr Sharawy 1 1 Biomedical Engineering, Cairo University, Giza, Egypt 2 College of Applied Medical Sciences, SALMAN Bin ABDUL-AZIZ University, Kharj, KSA 3 Biomedical Engineering, MUST University, 6 th of October, Egypt Abstract DNA microarray is a powerful tool and is widely used in genetics to monitor expression levels of thousands of genes in parallel. The gene expression process consists of three stages: gridding, segmentation and quantification. Grid- ding deals with finding areas in the microarray image which contain one spot using grid lines. This step can be done ma- nually or automatically. In this paper, we propose an efficient and simple automatic gridding method for microarray image analysis. This method was implemented using MATLAB software and found very effective for gridding arrays with low intensity, poor quality spotsand tested by a number of microarray images. Results show that this method gives high accu- racy of 76.9% improved to 98.6% when a preprocessing step is considered, rendering the method a promising technique for an efficient and automatic gridding the noisy microarray images. Keywords Microarray, Gene Expression, Gridding, Spot, Image Analysis 1. Introduction A DNA microarray is a powerful tool and is widely used in many research areas. For biologists, genetic research, understanding and diagnosis of cancer and many other dangerous diseases, as well as discovering treatments of diseases, are among the most interesting areas where DNA microarray analysis may be extremely helpful[1]. Tradi- tional methods in molecular biology generally work on one gene on a one-experiment basis, which means that the throughput is very limited and biologists can only be able to do such genetic analysis on a few genes at a time. Microar- ray technology makes it possible to measure the expression level of thousands of genes in a biological sample rapidly and efficiently on the slides[2]. A DNA microarray consists of a solid surface, onto which DNA molecules have been chemically bounded. The purpose of a microarray is to detect the presence and abun- dance of labelled nucleic acids in a biological sample, which will hybridize to the DNA on the array, and which can be detected via the label. In the majority of microarray experiments, the labelled nucleic acids are derived from the mRNA of a sample or tissue. Typically, control and test RNA samples are processed on the same array using two different dye tagged probes (e.g., the red fluorescent dye * Corresponding author: msm_eng@k-space.org (Mai Mabrouk) Published online at http://journal.sapub.org/ajbe Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved Cy5 and green fluorescent dye Cy3)[2-4], and so the mi- croarray measures gene expression. The next step is to produce an image, where the microar- ray is scanned by laser. By comparing the gene expression level in normal and diseased cells, it is found that this tool is really useful to identify diseased genes leading to accu- rate production of a therapeutic drug for that disease[5]. This gene expression process consists of three steps: 1) Gridding: This step seeks to find areas in the image which contain one spot using grid lines, i.e. to assign each spot to an indi- vidual compartment. 2) Segmentation: This step seeks to classify each compartment in the im- age into a foreground (spot) and a background area. 3) Quantification: This step seeks to calculate the intensity value of each spot. Gridding is the most fundamental and important step in the whole process of gene expression. There are various levels of image processing algorithms, which require a cer- tain level of user intervention for accurately gridding the microarray images. Grid alignment techniques can be viewed in terms of automation as manual, semiautomatic, and fully automated[6]. Major work has been presented in microarray image analysis. Roberto Hirata JR et al.[12] introduces a technique using morphological operators to perform automatic grid- ding procedures for sub grids and spots. Buhler et al.[13] describes a semi-automatic system which mainly focuses on