Abstract:
This study is an attempt to study the hippocampus (body, head, tail and
sagittal section) in MRI images using computerized textural analysis and
hence the main objectives of this study was to characterize the hippocampal
tissues into two classes normal and epileptic using textural analysis. The
texture were extracted from spatial gray level dependence matrix using a
window of 20×20 pixels of angle zero and distance equal one pixel. The
images were collected from MRI brain scans for 18 patients represent the
classes of the study in the period from 7/2011 to 2/2012. The images were
scored by an expert radiologist and the scoring was accepted in case of
agreement with findings of EEG. Then the features were extracted from the
selected sub-images that show only the region of interest. The classification
technique were adopted as a method of pattern identification the images into
normal of epileptic class. A linear discriminant analysis using stepwise were
used to classify the sample into the predefined classes. The stepwise selected
number of features out of fifteen features as the most discriminant features
for each hippocampal region; they included: Energy, Entropy, Correlation
Inertia, difference average, difference entropy, difference variance, sum
variance, um entropy, IDM, Information1 and information2. The result of
this study showed that the total classification accuracy was 83.3%, 80.6%,
91.7%, and 79.6% for body, head, tail and sagittal respectively. The
sensitivity was 72.2, 72.2, 94.4% and 79.6. The Specificity was 94.4%,
88.92%, 88.9% and 79.6% respectively.
