The paper introduces BrainSuite, an automated tool for identifying the cortical surface from magnetic resonance (MR) images of the human brain. The tool performs a sequence of low-level operations to produce accurate brain segmentations, including skull and scalp removal, image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The method aims to minimize user interaction and is designed to be fast and accurate. The authors describe the theory behind each stage of the cortical surface identification process and present validation results using real and phantom data. The tool is implemented as a stand-alone interactive application and has been validated on both phantom and real data, showing superior performance compared to existing methods. The results demonstrate that BrainSuite can produce topologically spherical cortical surface representations with minimal user interaction and in reasonable time on modern desktop hardware.The paper introduces BrainSuite, an automated tool for identifying the cortical surface from magnetic resonance (MR) images of the human brain. The tool performs a sequence of low-level operations to produce accurate brain segmentations, including skull and scalp removal, image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The method aims to minimize user interaction and is designed to be fast and accurate. The authors describe the theory behind each stage of the cortical surface identification process and present validation results using real and phantom data. The tool is implemented as a stand-alone interactive application and has been validated on both phantom and real data, showing superior performance compared to existing methods. The results demonstrate that BrainSuite can produce topologically spherical cortical surface representations with minimal user interaction and in reasonable time on modern desktop hardware.