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.

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