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