A comparative analysis of neural network methodologies for segmentation of magnetic resonance images

Author

Soltanian-Zadeh, Hamid

Volume

1

fYear

1996

fDate

16-19 Sep 1996

Firstpage

257

Abstract

Presents a comparative study of the potential of artificial neural networks for the segmentation of multispectral MRI data. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks have been applied to MR images as supervised segmentation methods. We have also applied analog adaptive resonance theory model (ART2) as an unsupervised segmentation method to MRI and have studied its function. We used identical data sets in this paper to compare the results. RBF proved the smallest execution time, but demonstrated more dependency on the training data than MLP. ART2 provided a good unsupervised technique for the MRI data

Keywords

ART neural nets; biomedical NMR; feedforward neural nets; image segmentation; medical image processing; multilayer perceptrons; unsupervised learning; analog adaptive resonance theory model; artificial neural networks; comparative analysis; execution time; identical data sets; magnetic resonance images; multilayer perceptron; multispectral MRI data; neural network methodologies; radial basis function; segmentation; supervised segmentation methods; training data; unsupervised segmentation method; Artificial neural networks; Image analysis; Image segmentation; Magnetic analysis; Magnetic resonance; Magnetic resonance imaging; Multilayer perceptrons; Neural networks; Radial basis function networks; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1996. Proceedings., International Conference on

Conference_Location

Lausanne

Print_ISBN

0-7803-3259-8

Type

conf

DOI

10.1109/ICIP.1996.560766

Filename

560766