We consider the problem of variable selection in classification for high dimensional spatially correlated data. Identification of reliable classification patterns is challenging due to the large number of variables, the small sample size, and spatial correlation among variables. In the motivating example of fMRI data, each variable represents the brain signal from one voxel in response to the stimuli, which tends to be spatially correlated with nearby voxels. Ignoring the spatial correlation may prevent inclusion of correlated variables in the model. Thus we propose a Bayesian probit model with spatially varying coefficients. We further develop a region selection strategy and demonstrate with simulation and real data that the proposed approach is effective in selection of the clustered true variables even with high correlation while achieving sparsity. If time allows, a supervised classification algorithm for segmenting multiple sclerosis lesions, which integrates the intensity information from multiple MRI modalities, the texture information, and the spatial information in a Bayesian framework will be presented. A weighted regularized regression model for imaging mass spectrometry data biomarker selection and classification will also be discussed.