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Feature Selection Based on Machine Learning in MRIs for Hippocampal Segmentation
- Source :
- Computational and Mathematical Methods in Medicine, Computational and mathematical methods in medicine (Online) 2015 (2015). doi:10.1155/2015/814104, info:cnr-pdr/source/autori:Sabina Tangaro, Nicola Amoroso, Massimo Brescia, Stefano Cavuoti, Andrea Chincarini, Rosangela Errico, Paolo Inglese, Giuseppe Longo, Rosalia Maglietta, Andrea Tateo, Giuseppe Riccio, Roberto Bellotti/titolo:Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation/doi:10.1155%2F2015%2F814104/rivista:Computational and mathematical methods in medicine (Online)/anno:2015/pagina_da:/pagina_a:/intervallo_pagine:/volume:2015, Computational and Mathematical Methods in Medicine, Vol 2015 (2015)
- Publication Year :
- 2015
- Publisher :
- Hindawi Publishing Corporation, 2015.
-
Abstract
- 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.<br />Comment: To appear on "Computational and Mathematical Methods in Medicine", Hindawi Publishing Corporation. 19 pages, 7 figures
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Physics - Medical Physic
Computer Science - Computer Vision and Pattern Recognition
computer.software_genre
Hippocampus
Machine Learning (cs.LG)
Pattern Recognition, Automated
Machine Learning
Voxel
Image Processing, Computer-Assisted
Physic
Medical Physic
medicine.diagnostic_test
Applied Mathematics
General Medicine
Magnetic Resonance Imaging
Random forest
Feature (computer vision)
Modeling and Simulation
lcsh:R858-859.7
Computer Vision and Pattern Recognition
MILD COGNITIVE IMPAIRMENT, MAMMOGRAPHIC DATABASE, ALZHEIMERS-DISEASE, VALIDATION, CLASSIFICATION
MRI
Research Article
Article Subject
FOS: Physical sciences
Feature selection
lcsh:Computer applications to medicine. Medical informatics
General Biochemistry, Genetics and Molecular Biology
Set (abstract data type)
medicine
Learning
Humans
General Immunology and Microbiology
business.industry
Computational Biology
Pattern recognition
Magnetic resonance imaging
Filter (signal processing)
Physics - Medical Physics
Computer Science - Learning
Independent set
Computer Science
Artificial intelligence
Medical Physics (physics.med-ph)
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 17486718 and 1748670X
- Volume :
- 2015
- Database :
- OpenAIRE
- Journal :
- Computational and Mathematical Methods in Medicine
- Accession number :
- edsair.doi.dedup.....18906e7c0f234f84ea40706b3fda36ad