Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels for thousands of genes simultaneously. Image analysis is an important aspect of microarray experiments, one which can have a potentially large impact on subsequent analyses such as clustering or the identification of differentially expressed genes. This paper reviews a number of existing image analysis methods used on cDNA microarray data and compares their effects on the measured log ratios of fluorescence intensities. In particular, this study examines the statistical properties of different segmentation and background adjustment methods. The different image analysis methods are applied to microarray data from a study of lipid metabolism in mice. We show that in some cases background adjustment can substantially reduce the precision - that is, increase the variability - of low-intensity spot values. In contrast, the choice of segmentation procedure has a smaller impact.
In addition, this paper proposes new addressing, segmentation and background correction methods for extracting information from microarray images. The segmentation component uses a seeded region growing algorithm which makes provision for spots of different shapes and sizes. The background estimation approach uses an image analysis technique known as morphological opening. All these methods are implemented in a software package named Spot.