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In this paper, correlation of the pixels comprising a microarray spot is investigated. Subsequently, correlation statistics, namely, Pearson correlation and Spearman rank correlation, are used to segment the foreground and background intensity of microarray spots. The performance of correlation-based segmentation is compared to clustering-based (PAM, k-means) and seeded-region growing techniques (SPOT). It is shown that correlation-based segmentation is useful in flagging poorly hybridized spots, thus minimizing false-positives. The present study also raises the intriguing question of whether a change in correlation can be an indicator of differential gene expression.
Microarrays, image segmentation, Morgera's covariance complexity, Pearson's correlation, Spearman's rank correlation.

R. Nagarajan and M. Upreti, "Correlation Statistics for cDNA Microarray Image Analysis," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. , pp. 232-238, 2006.
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