Classification of magnetic resonance images

Author

Trojacanec, Katarina ; Madzarov, Gjorgji ; Gjorgjevikj, Dejan ; Loskovska, Suzana

Author_Institution

Fac. of Electr. Eng. & Inf. Technol., Ruger Boskovik bb., Skopje, Macedonia

fYear

2010

fDate

21-24 June 2010

Firstpage

597

Lastpage

602

Abstract

The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are applied to provide feature extraction from the images. The dataset used for classification contains magnetic resonance images classified in 9 classes.

Keywords

feature extraction; image classification; magnetic resonance imaging; neural nets; support vector machines; MRI; feature extraction; image classification; k nearest neighbor classifier; magnetic resonance image; neural networks; support vector machine; Artificial neural networks; Classification algorithms; Feature extraction; Histograms; Magnetic resonance; Magnetic resonance imaging; Support vector machines; Classification; Magnetic Resonance Images (MRIs); Support Vector Machines (SVMs); neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology Interfaces (ITI), 2010 32nd International Conference on

Conference_Location

Cavtat/Dubrovnik

ISSN

1330-1012

Print_ISBN

978-1-4244-5732-8

Type

conf

Filename

5546464