Computer aid to diagnostic in 
epilepsy and 
Alzheimer's disease

Computer aided diagnosis in neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This book proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and make use of cross-sectional scans scans: appearance-based classification (ABC) and voxel-based classification (VBC). The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses,in multiple cross-validated trials. Applications are shown for the lateralization of seizure focus in temporal lobe epilepsy, as well as classification in Alzheimer's disease.