An image-processing strategy for functional magnetic resonance imaging (fMRI) data sets consisting of sequential images of the same slice of brain tissue is considered. An algorithm of detection based on the likelihood-ratio test and the noise properties in fMRI is introduced. Since the data have a poor signal-to-noise ratio, and in order to make detection reliable, the algorithm is organized in two steps: (1) pixel detection, which detects all pixels having significant changes, thus building regions of interest (ROIs), and (2) region detection, which selects the most likely activated region from obtained ROIs. The detection method is applied to experimental fMRI data from the motor cortex and compared with the cross-correlation method and Student's t test commonly applied by others. The results obtained using the likelihood-ratio test show improvement in the detection of activated regions.