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Detecting weak clustered signal in spatial data is important but challengingin applications such as medical image and epidemiology. A more efficientdetection algorithm can provide more precise early warning, and effectivelyreduce the decision risk and cost. To date, many methods have been developed todetect signals with spatial structures. However, most of the existing methodsare either too conservative for weak signals or computationally too intensive.In this paper, we consider a novel method named Spatial CUSUM (SCUSUM), whichemploys the idea of the CUSUM procedure and false discovery rate controlling.We develop theoretical properties of the method which indicates thatasymptotically SCUSUM can reach high classification accuracy. In the simulationstudy, we demonstrate that SCUSUM is sensitive to weak spatial signals. Thisnew method is applied to a real fMRI dataset as illustration, and moreirregular weak spatial signals are detected in the images compared to someexisting methods, including the conventional FDR, FDR$_L$ and scan statistics.

Spatial CUSUM for Signal Region Detection