Cerebrovascular disease (CVD) is a very significant health problem, especially in view of the increasing aging population. It is highly prevalent in diabetic populations and is also a major cause of dementia, individually or as an additive factor to other pathologies, such as Alzheimer’s. Therefore measuring disease burden, progression, and response to treatments is very important for patient management. MRI is currently the most widely used way to characterize in vivo the type and extent of brain lesions in CVD and has been used in several large neuroimaging studies. However, characterization of CVD has largely relied on qualitative, subjective, and not easily reproducible methods of human expert-based interpretation. Computer-based methods for measuring CVD offer great potential, since they are quantitative; reproducible, and particularly suitable for longitudinal studies monitoring disease progression and response to candidate treatments. Most methods have focused on the segmentation of Multiple Sclerosis lesions, whereas less attention has been given to brain lesion segmentation in elderly individuals and Alzheimer’s disease or diabetic patients. The project DeBrA (Detection of Brain Abnormality) includes the development and implementation of medical imaging techniques for measuring CVD and its change over time, from multi-parametric MR images by employing advanced 4-dimensional (space X time) segmentation methods based on pattern classification and statistical modeling. The goal is to determine whether the new methodology will provide more stable measurements of longitudinal change in CVD, compared to relatively more conventional methods and therefore increase the sensitivity of detecting subtle effects. Moreover, while the tools will be initially developed for measuring CVD, they will be made suitable to hold widespread potential for applications in other neuroimaging studies involving abnormality detection.