In this paper a feasibility study of brain MRI data
set classification, using ROIs
which have been segmented either manually or throug
h a superpixel based method in
conjunction with statistical pattern recognition me
thods is presented. In our study,
471 extracted ROIs from 21 Brain MRI datasets are u
sed, in order to establish which
features distinguish better between three grading c
lasses. Thirty-eight statistical
measurements were collected from the ROIs. We found
by using the Leave-One-Out
method that the combination of the features from th
e 1
st
and 2
nd
order statistics,
achieved high classification accuracy in pair-wise
grading comparisons.