Lossless Microarray Image Compression using Region Based Predictors

Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces large amount of image data, hence efficient compression routines that exploit microarray image structures are required. In this paper we introduce a lossless image compression method which segments the pixels of the image into three categories of background, foreground, and spot edges. The segmentation is performed by finding a threshold value which minimizes the weighted sum of the standard deviations of the foreground and background pixels. Each segment of the image is compressed using a separate predictor. The results of the implementation of the method show its superiority compared to the well-known microarray compression schemes as well as to the general lossless image compression standards.