The delineation of brain lesion boundaries in computerized tomography (CT) or magnetic resonance imaging (MRI) sequences is important in many medical research environments and clinical applications. For example, computer-aided neurosurgery requires the extraction of boundaries of lesions in a series of CT or MRI images in order to design the surgical trajectory and complete the surgical planning. Currently, in many clinical applications, the boundaries of lesions are traced manually. Manual methods are not only tedious but also subjective, leading to substantial inter- and intraobserver variability, and confusions between lesions and coexisting normal structures pose serious problems. Automatic detection of lesions is a nontrivial problem. Because of the low resolution, the border regions between lesions and normal tissues are typically of single-pixel width in CT images, and the intensity gradient at the lesion boundary varies considerably. These characteristics of lesions within CT images, in conjunction with the generally low signal-to-noise ratio of CT images, render simple boundary detection techniques inadequate. Recent work in the field of computer vision has shown multiscale analysis of objects in gray scale images to be effective in many applications. This paper describes and illustrates the application of multiscale morphological techniques to the delineation of brain tumors.