Self-organized feature detection and segmentation of magnetic resonance images
Abstract
Unsupervised, competitive learning was applied to a self-organizing map for feature detection, and tissue segmentation of magnetic resonance images of the brain. The multi-spectral input data were the individual pixel intensities from T1-weighted, T2-weighted, and proton density MR images. The technique trained quickly and generalized to other slices from the same study. Pathologies were detected, and white matter, gray matter, and cerebral spinal fluid were segmented.
Publication Title
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Recommended Citation
Deaton, R., Sun, J., & Reddick, W. (1994). Self-organized feature detection and segmentation of magnetic resonance images. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (pt 1), 602-603. Retrieved from https://digitalcommons.memphis.edu/facpubs/14205
