Neural network methods for volumetric magnetic resonance imaging of the human brain

Author

Gelenbe, Erol ; Feng, Yutao ; Krishnan, K. Ranga R

Author_Institution

Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA

Volume

84

Issue

10

fYear

1996

fDate

10/1/1996 12:00:00 AM

Firstpage

1488

Lastpage

1496

Abstract

Brain magnetic resonance (MR) images contain massive information requiring lengthy and complex interpretation (as in the identification of significant portions of the image), quantitative evaluation (as in the determination of the size of certain significant regions), and sophisticated interpretation (as in determining any image portions which indicate signs of lesions or of disease). In this paper we first survey the clinical and research needs for brain imaging. We present the state-of-the-art in relevant image analysis techniques. We then discuss our recent work on the use of novel artificial neural networks which have a recurrent structure to extract precise morphometric information from MRI scans of the human brain. Finally, experimental data using our novel approach is presented and suggestions are made for future research

Keywords

NMR imaging; brain; medical image processing; neurophysiology; recurrent neural nets; MRI scans; human brain; image analysis; interpretation; morphometric information extraction; quantitative evaluation; recurrent neural network; volumetric magnetic resonance imaging; Artificial neural networks; Biological neural networks; Brain; Data mining; Diseases; Image analysis; Lesions; Magnetic resonance; Magnetic resonance imaging; Neural networks;

fLanguage

English

Journal_Title

Proceedings of the IEEE

Publisher

ieee

ISSN

0018-9219

Type

jour

DOI

10.1109/5.537113

Filename

537113