  Neurodegenerative diseases are frequently associated with structural changes
in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations
and therefore be used as a supportive feature for a number of neurodegenerative
diseases. The hippocampus has been known to be a biomarker for Alzheimer
disease and other neurological and psychiatric diseases. However, it requires
accurate, robust and reproducible delineation of hippocampal structures. Fully
automatic methods are usually the voxel based approach, for each voxel a number
of local features were calculated. In this paper we compared four different
techniques for feature selection from a set of 315 features extracted for each
voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper
methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential
Backward Elimination; and (iv) embedded method based on the Random Forest
Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent
set of 25 subjects. The resulting segmentations were compared with manual
reference labelling. By using only 23 features for each voxel (sequential
backward elimination) we obtained comparable state of-the-art performances with
respect to the standard tool FreeSurfer.

Medical Physics;Computer Vision and Pattern Recognition;Machine Learning