MR image segmentation using a fuzzy-based neural network

Research output: Contribution to conferencePaper

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 feed-forward 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, 3-D magnetic resonance images are presented, demonstrating the performance of the segmentation as well as the final surface rendering.

Original languageEnglish (US)
Pages2190-2195
Number of pages6
StatePublished - Dec 1 1995
EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust
Duration: Nov 27 1995Dec 1 1995

Other

OtherProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
CityPerth, Aust
Period11/27/9512/1/95

ASJC Scopus subject areas

  • Software

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  • Cite this

    Ma, J. C., & Rodriguez, J. J. (1995). MR image segmentation using a fuzzy-based neural network. 2190-2195. Paper presented at Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6), Perth, Aust, .