In this study, we used a small implantable complementary metal–oxide–semiconductor (CMOS) imaging device developed by our research group to estimate the blood flow changes by focusing on the movement of red blood cells in the captured movements. Conventional methods for determining blood flow velocity have limitations in that variation occurs due to manual measurement. Therefore, we developed a novel technique to measure blood flow in the brain. This method involves calculating the changes of selected pixels using a normalized cross-correlation coefficient. Using this cross-correlation method, we analyzed a mouse cerebral infarction model to detect changes in brain activity. The results of analysis showed that the average velocity of blood flow upstream of the infarction site decreased while the velocity in blood vessel parallel to the infarction increased after occlusion was induced. These results thus confirmed that the new method can detect blood velocity changes, suggesting the feasibility of the cross-correlation method for estimating blood flow velocity.