In this paper, we address the NSPS (a Neurological Surgery Planning System developed at the Neurological Surgery Department of Wayne State University) approaches for segmenting and representing lesions in MRI brain images. Initially, the 2D segmentation algorithm requires the input of a seed (an individual pixel or a small region) and a threshold to control the formation of a lesion region. The 3D segmentation algorithm requires the input of a seed, along with the threshold computed automatically from the corresponding three sample thresholds of lesion regions in sagittal, coronal, and axial views, to form a lesion volume. Then, a novel method is developed to represent the segmented lesion regions with feature point histograms, obtained by discretizing and counting the angles produced from the resulting Delaunay triangulation of a set of feature points which characterize the shape of the lesion region. The proposed shape representation technique is translation, scale, and rotation independent. Through various experiment results, we demonstrate the efficacy of the NSPS methodologies. Finally, based on the lesion representation scheme, we present a prototype system architecture for neurological surgery training. The implemented system will work in a Web-based environment, allowing neurosurgeons to query and browse various patients-related medical records in an effective and efficient way.