BACKGROUND: Image analysis is the first crucial step to obtain reliable results from microarray experiments. First, areas in the image belonging to single spots have to be identified. Then, those target areas have to be partitioned into foreground and background. Finally, two scalar values for the intensities have to be extracted. These goals have been tackled either by spot shape methods or intensity histogram methods, but it would be desirable to have hybrid algorithms which combine the advantages of both approaches. RESULTS: A new robust and adaptive histogram type method is pixel clustering, which has been successfully applied for detecting and quantifying microarray spots. This paper demonstrates how the spot shape can be effectively integrated in this approach. Based on the clustering results, a bivalence mask is constructed. It estimates the expected spot shape and is used to filter the data, improving the results of the cluster algorithm. The quality measure 'stability' is defined and evaluated on a real data set. The improved clustering method is compared with the established Spot software on a data set with replicates. CONCLUSION: The new method presents a successful hybrid microarray image analysis solution. It incorporates both shape and histogram features and is specifically adapted to deal with typical microarray image characteristics. As a consequence of the filtering step pixels are divided into three groups, namely foreground, background and deletions. This allows a separate treatment of artifacts and their elimination from the further analysis.

MOTIVATION: Microarray images challenge existing analytical methods in many ways given that gene spots are often comprised of characteristic imperfections. Irregular contours, donut shapes, artifacts, and low or heterogeneous expression impair corresponding values for red and green intensities as well as their ratio R/G. New approaches are needed to ensure accurate data extraction from these images. RESULTS: Herein we introduce a novel method for intensity assessment of gene spots. The technique is based on clustering pixels of a target area into foreground and background. For this purpose we implemented two clustering algorithms derived from k-means and Partitioning Around Medoids (PAM), respectively. Results from the analysis of real gene spots indicate that our approach performs superior to other existing analytical methods. This is particularly true for spots generally considered as problematic due to imperfections or almost absent expression. Both PX(PAM) and PX(KMEANS) prove to be highly robust against various types of artifacts through adaptive partitioning, which more correctly assesses expression intensity values. AVAILABILITY: The implementation of this method is a combination of two complementary tools Extractiff (Java) and Pixclust (free statistical language R), which are available upon request from the authors.