Voxel-Based Morphometry (VBM) is a method for comparing gray matter concentration differences between two groups of subjects. The process involves spatially normalizing high-resolution images to a stereotactic space, segmenting gray matter from these normalized images, smoothing the gray-matter segments, and performing voxel-wise parametric statistical tests to compare the smoothed images. Corrections for multiple comparisons are made using Gaussian random field theory. This paper details the steps in VBM, focusing on segmenting gray matter from MR images with nonuniformity artifacts. It evaluates the assumptions underlying the method, including the accuracy of segmentation and the assumptions about data distribution. The paper also discusses limitations and future directions, emphasizing the importance of accurate segmentation and the need to address nonnormality in data distribution.Voxel-Based Morphometry (VBM) is a method for comparing gray matter concentration differences between two groups of subjects. The process involves spatially normalizing high-resolution images to a stereotactic space, segmenting gray matter from these normalized images, smoothing the gray-matter segments, and performing voxel-wise parametric statistical tests to compare the smoothed images. Corrections for multiple comparisons are made using Gaussian random field theory. This paper details the steps in VBM, focusing on segmenting gray matter from MR images with nonuniformity artifacts. It evaluates the assumptions underlying the method, including the accuracy of segmentation and the assumptions about data distribution. The paper also discusses limitations and future directions, emphasizing the importance of accurate segmentation and the need to address nonnormality in data distribution.