MR image segmentation using a fuzzy-based neural network

Author

Ma, Jesse C. ; Rodríguez, Jeffrey J.

Author_Institution

Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ, USA

Volume

5

fYear

1995

fDate

Nov/Dec 1995

Firstpage

2190

Abstract

Most techniques for segmentation of magnetic resonance images of the brain are extremely time consuming and/or require extensive user interaction. An automated segmentation procedure is presented, whereby the fuzzy c-means classification results are used to train a feedforward neural network. The cascade correlation algorithm is used to optimize the network training process. After applying a brain-extraction technique, the segmented images are then used for rendering computer-generated images of the brain´s surface. Experimental results using real, 3D magnetic resonance images are presented, demonstrating the performance of the segmentation as well as the final surface rendering

Keywords

biomedical NMR; brain; feedforward neural nets; fuzzy neural nets; image classification; image segmentation; medical image processing; 3D MR brain images; MR image segmentation; cascade correlation; feedforward neural network; fuzzy c-means classification; fuzzy neural network; image classification; magnetic resonance images; neurophysiology; surface rendering; Biological neural networks; Clustering algorithms; Computer networks; Data visualization; Fuzzy neural networks; Image segmentation; Magnetic heads; Magnetic resonance; Neural networks; Rendering (computer graphics);

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.1995.487700

Filename

487700