Classification of MR and CT images using genetic algorithms

Author

Dokur, Zumray ; Olmez, Tamer ; Yazgan, Ertugrul

Author_Institution

Dept. of Electron. & Commun. Eng., Istanbul Tech. Univ., Turkey

Volume

3

fYear

1998

fDate

29 Oct-1 Nov 1998

Firstpage

1418

Abstract

A modified restricted Coulomb energy (MoRCE) network trained by the genetic algorithm is presented. Each neuron of the network forms a closed region in the input space. The closed regions which are formed by the neurons overlap each other, like STAR. Genetic algorithms are used to improve the classification performances of the magnetic resonance (MR) and computer tomography (CT) images with minimized number of neurons. MoRCE is examined comparatively with multilayer perceptron (MLP), and restricted Coulomb energy (RCE). It is observed that MoRCE gives the best classification performance with less number of neurons after a short training time

Keywords

biomedical MRI; computerised tomography; genetic algorithms; image classification; learning (artificial intelligence); medical expert systems; medical image processing; neural nets; MRI images; closed region; computer tomography images; genetic algorithm; hyperspheres; image classification performance; input space; minimized number of neurons; modified restricted Coulomb energy network; multilayer perceptron comparison; short training time; supervised learning; Chromium; Computed tomography; Electronic mail; Genetic algorithms; Genetic engineering; Magnetic multilayers; Magnetic resonance; Neural networks; Neurons; Power engineering and energy;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE

Conference_Location

Hong Kong

ISSN

1094-687X

Print_ISBN

0-7803-5164-9

Type

conf

DOI

10.1109/IEMBS.1998.747149

Filename

747149