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 language | English (US) |
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| Pages | 2190-2195 |
| Number of pages | 6 |
| State | Published - 1995 |
| Event | Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust Duration: Nov 27 1995 → Dec 1 1995 |
Other
| Other | Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) |
|---|---|
| City | Perth, Aust |
| Period | 11/27/95 → 12/1/95 |
ASJC Scopus subject areas
- Software