Unsupervised segmentation of multi-echo MR images with an ART-based neural network

Author

Li, Wanqing ; Attikouzel, Y.

Author_Institution

Centre for Intelligent Inf. Process. Syst., Western Australia Univ., Nedlands, WA, Australia

Volume

5

fYear

1995

fDate

Nov/Dec 1995

Firstpage

2600

Abstract

This paper investigates the suitability of an ART-based neural network for unsupervised segmentation of multi-echo MR images. The ART2A network was used to segment standard dual-echo MR images. Two problems were identified with the basic ART2A: one, the network was hardly convergent; and two, the categorization depended on the order of presentation of the patterns. In order to solve these two problems, a dynamic learning parameter and random pattern presentation method were introduced. Results using a number of actual dual-echo MR images with the modified ART2A network show that ART-based networks can be used for segmentation of multi-echo MR images

Keywords

ART neural nets; biomedical NMR; image segmentation; medical image processing; unsupervised learning; ART-based neural network; ART2A network; dynamic learning parameter; multi-echo MR images; random pattern presentation method; unsupervised segmentation; Adaptive filters; Biological neural networks; Data mining; Humans; Image converters; Image segmentation; Information processing; Neural networks; Pathology; Subspace constraints;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.487819

Filename

487819