cDNA micro arrays are more and more frequently used in molecular biology as they can give insight into the relation of an organism's metabolism and its genome. The process of imaging a micro array sample can introduce a great deal of noise and bias into the data with higher variance than the original signal which may swamp the useful information. As imperfections and fabrication artifacts often impair our ability to measure accurately the quantities of interest in micro array images, image processing for analysis of these images is an important and challenging problem. How to eliminate the effect of the noise imposes a challenging problem in micro array analysis. In this paper we implemented a novel algorithm for image sifting which could remove objects with definite size from macro array images. We used regular moving grids to sift noise object and obtained clean images for segmentation. The results have been compared with SWT, DWT and wiener filter denosing. Micro arrays have become the tool of choice for the global analysis of gene expression. Powerful statistical tools are now available to analyze this expression and to gain an understanding of how changes in gene expression patterns impact biological systems. Currently, several different platforms have evolved from the origin of this imaging technique which goes back to the 1970's (1). The analysis of such data has become a computationally- intensive task that requires technological developments at various stages, from the design of the array, to image analysis, database storage, data processing and clustering and information extraction. Further innovations were made by M. Schena et al (2), by using nonporous solid support to facilitate miniaturization and fluorescent-based detection. Another improvement was the methods introduced by S. Fodor et al. (3), for the high density spatial synthesis oligonucleotide which makes it possible to monitor changes in the expression patterns of thousands of genes. Image analysis is an important aspect for micro array experiments that can affect subsequent analysis such as identification of differentially expressed genes. Image processing for micro array images includes three tasks: spot gridding, segmentation and information extraction. In recent years, large number of commercial tools has been developed in micro array image processing (4, 5, 6, 7, and 8)