Title
Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation.
Abstract
Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can 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 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.
Year
DOI
Venue
2015
10.1155/2015/814104
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Voxel,Feature selection,Computer science,Artificial intelligence,Random forest,Hippocampal formation,Forward selection,Computer vision,Pattern recognition,Segmentation,Independent set,Machine learning,Magnetic resonance imaging
Journal
abs/1501.03915
ISSN
Citations 
PageRank 
1748-670X
3
0.48
References 
Authors
17
12