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.