You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

Graph theory for feature extraction and classification: A migraine pathology case study

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Jorge-Hernandez, Fernando; | Garcia Chimeno, Yolanda | Garcia-Zapirain, Begonya | Cabrera Zubizarreta, Alberto | Gomez Beldarrain, Maria Angeles | Fernandez-Ruanova, Begonya

Affiliations: Deustotech Life Department, University of Deusto, Bilbao 48007, Spain | Department of Neuroradiology, Osatek, Galdakao 48960, Spain | Department of Neurology, Galdakao-Usansolo Hospital, Galdaka 48960, Spain | Research Department, Osatek, Bilbao 48011, Spain

Note: [] Corresponding author: Fernando Jorge-Hernandez, Deustotech Life Department, University of Deusto, Bilbao 48007, Spain. Tel.: +34 944 13 90 00; Fax: +34 944 45 68 17; E-mail: [email protected].

Keywords: Functional MRI (fMRI), graph theory, migraine, machine learning, synchronization likelihood

DOI: 10.3233/BME-141118

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 2979-2986, 2014

Published: 2014
Get PDF open access

Abstract

Graph theory is also widely used as a representational form and characterization of brain connectivity network, as is machine learning for classifying groups depending on the features extracted from images. Many of these studies use different techniques, such as preprocessing, correlations, features or algorithms. This paper proposes an automatic tool to perform a standard process using images of the Magnetic Resonance Imaging (MRI) machine. The process includes pre-processing, building the graph per subject with different correlations, atlas, relevant feature extraction according to the literature, and finally providing a set of machine learning algorithms which can produce analyzable results for physicians or specialists. In order to verify the process, a set of images from prescription drug abusers and patients with migraine have been used. In this way, the proper functioning of the tool has been proved, providing results of 87% and 92% of success depending on the classifier used.

Show more