  Most of the temporal lobe epilepsy detection approaches are based on
hippocampus deformation and use complicated features, resulting, detection is
done with complicated features extraction and pre-processing task. In this
paper, a new detection method based on shape-based features and spherical
harmonics is proposed which can analysis the hippocampus shape anomaly and
detection asymmetry. This method consisted of two main parts; (1) shape feature
extraction, and (2) image classification. For evaluation, HFH database is used
which is publicly available in this field. Nine different geometry and 256
spherical harmonic features are introduced then selected Eighteen of them that
detect the asymmetry in hippocampus significantly in a randomly selected subset
of the dataset. Then a support vector machine (SVM) classifier was employed to
classify the remaining images of the dataset to normal and epileptic images
using our selected features. On a dataset of 25 images, 12 images were used for
feature extraction and the rest 13 for classification. The results show that
the proposed method has accuracy, specificity and sensitivity of, respectively,
84%, 100%, and 80%. Therefore, the proposed approach shows acceptable result
and is straightforward also; complicated pre-processing steps were omitted
compared to other methods.
