Arterial spin labeling (ASL) provides a noninvasive measurement of cerebral blood flow (CBF). Due to relatively low spatial resolution, the accuracy of CBF measurement is affected by the partial volume (PV) effect. To obtain accurate CBF estimation, the contribution of each tissue type in the mixture is desirable. In general, this can be obtained according to the registration of ASL and structural image in current ASL studies. This approach can obtain probability of each tissue type inside each voxel, but it also introduces error, which include error of registration algorithm and imaging itself error in scanning of ASL and structural image. Therefore, estimation of mixture percentage directly from ASL data is greatly needed. Under the assumption that ASL signal followed the Gaussian distribution and each tissue type is independent, a maximum a posteriori expectation-maximization (MAP-EM) approach was formulated to estimate the contribution of each tissue type to the observed perfusion signal at each voxel. Considering the sensitivity of MAP-EM to the initialization, an approximately accurate initialization was obtain using 3D Fuzzy c-means method. Our preliminary results demonstrated that the GM and WM pattern across the perfusion image can be sufficiently visualized by the voxel-wise tissue mixtures, which may be promising for the diagnosis of various brain diseases.