Three-dimensional MRI segmentation based on back-propagation neural network with robust supervised training

Jorge U. Garcia; Leopoldo Gonzalez-Santos; Rafael Favila; Rafael Rojas; Fernando A. Barrios

doi:10.1117/12.387745

6 June 2000 Three-dimensional MRI segmentation based on back-propagation neural network with robust supervised training

Jorge U. Garcia, Leopoldo Gonzalez-Santos, Rafael Favila, Rafael Rojas, Fernando A. Barrios

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387745
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

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.

Citation Download Citation

Jorge U. Garcia, Leopoldo Gonzalez-Santos, Rafael Favila, Rafael Rojas, and Fernando A. Barrios "Three-dimensional MRI segmentation based on back-propagation neural network with robust supervised training", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387745

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