Share Email Print
cover

Proceedings Paper

Three-dimensional MRI segmentation based on back-propagation neural network with robust supervised training
Author(s): Jorge U. Garcia; Leopoldo Gonzalez-Santos; Rafael Favila; Rafael Rojas; Fernando A. Barrios
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

An image segmentation algorithm based on back-propagation neural network with robust supervised training, is presented. Using this algorithm it is possible to do brain MRI segmentation with good resolution between white and gray matter and recognition of some structures. Initial weight parameter evaluation takes fair amount of computational time resulting in a fast slice segmentation once the network has been trained. The training step consists of choosing a set of optimal weights for interchanging network nodes such that when the values of gray level patterns are presented to the network, it classifies them for different tissue types.

Paper Details

Date Published: 6 June 2000
PDF: 8 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387745
Show Author Affiliations
Jorge U. Garcia, Univ. Nacional Autonoma de Mexico and Univ. Virtual (Mexico)
Leopoldo Gonzalez-Santos, Univ. Nacional Autonoma de Mexico (Mexico)
Rafael Favila, Univ. Nacional Autonoma de Mexico (Mexico)
Rafael Rojas, Hospital ABC (Mexico)
Fernando A. Barrios, Univ. Nacional Autonoma de Mexico (Mexico)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

© SPIE. Terms of Use
Back to Top