DMCA
Neural Network based Segmentation of Magnetic Resonance Images of the Brain (1997)
| Venue: | IEEE Nuclear Science Symposium & Medical Imaging Conference 3 |
| Citations: | 12 - 0 self |
BibTeX
@INPROCEEDINGS{Alirezaie97neuralnetwork,
author = {Javad Alirezaie and M. E. Jernigan and C. Nahmias},
title = {Neural Network based Segmentation of Magnetic Resonance Images of the Brain},
booktitle = {IEEE Nuclear Science Symposium & Medical Imaging Conference 3},
year = {1997},
pages = {1397--1401}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
This paper presents a study investigating the potential of artificial neural networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a Learning Vector Quantization (LVQ) Artificial Neural Network (ANN) for the multispectral supervised classification of MR images. We have modified the LVQ for better and more accurate classification. We have compared the results using LVQ ANN versus back-propagation ANN. This comparison shows that, unlike back-propagation ANN, our method is insensitive to the gray-level variation of MR images between different slices. It shows that tissue segmentation using LVQ ANN also performs better and faster than that using back-propagation ANN. I. Introduction Segmentation of images obtained from magnetic resonance (MR) imaging techniques is an important step in the analysis of MR images of the human body. MR imaging has a unique advantage over other modalities in t...
Keyphrases
back-propagation ann mr image magnetic resonance image neural network magnetic resonance artificial neural network lvq ann multispectral supervised classification different slice tissue segmentation gray-level variation accurate classification mr imaging human brain unique advantage introduction segmentation important step human body learning vector quantization
