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Motivated by the problem of colocalization analysis in fluorescencemicroscopic imaging, we study in this paper structured detection of correlatedregions between two random processes observed on a common domain. We argue thatalthough intuitive, direct use of the maximum log-likelihood statistic suffersfrom potential bias and substantially reduced power, and introduce a simplesize-based normalization to overcome this problem. We show that scanning withthe proposed size-corrected likelihood ratio statistics leads to optimalcorrelation detection over a large collection of structured correlationdetection problems.

Structured Correlation Detection with Application to Colocalization Analysis in Dual-Channel Fluorescence Microscopic Imaging