  In this report, 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 minimizes false-positives. The
present study also raises the intriguing question of whether a change in
correlation can be an indicator of differential gene expression.
