LAUSR

Microarray technology, which can monitor the expression levels of thousands of genes simultaneously, has been widely used in biological experiment. Image processing, as one key step in microarray technology, plays an essential role in microarray analysis. Meanwhile, biological applications require a higher accuracy in each image processing step. However, the low contrast levels of image make it difficult to obtain better processing precision. This paper proposes a fully automatic contrast enhancement (CE) method based on fourth-order moment. Also, a background estimation method is presented to obtain a better microarray image processing result. Comparative results on contrast enhance and gridding revealed that the proposed CE algorithm performs better compared to the adaptive histogram equalization method. Numerous experiments on the Swiss Institute of Bioinformatics (SIB), Joe DeRisi individual (DeRisi), Gene Expression Omnibus (GEO), and Stanford Microarray Database (SMD) data sets also indicate that the proposed CE exerts a tremendous effect on gridding, but has nothing to do with segmentation.