Segmentation of multidimensional magnetic resonance (MR) images using a fuzzy neural network

Jesse C. Ma; Jeffrey J. Rodriguez

doi:10.1117/12.186578

21 September 1994 Segmentation of multidimensional magnetic resonance (MR) images using a fuzzy neural network

Jesse C. Ma, Jeffrey J. Rodriguez

Proceedings Volume 2298, Applications of Digital Image Processing XVII; (1994) https://doi.org/10.1117/12.186578
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

Methods of 3-D visualization of the brain based on fuzzy c-means (FCM) classified magnetic resonance (MR) images and a neural network trained on the FCM data are presented. A 3-D MR scan of a volunteer serves as the basis for the unsupervised classification techniques. The images were first classified into different tissue types by using FCM. The classified images were then reconstructed for 3-D display. Results show that individual tissue types can be discriminated during the 3-D rendering process. A neural network trained on the fuzzy classification data was also implemented. By using the cascade correlation algorithm during the network training, much of the tedious training work was avoided. The preliminary results from the neural network approach are quite encouraging.

Citation Download Citation

Jesse C. Ma and Jeffrey J. Rodriguez "Segmentation of multidimensional magnetic resonance (MR) images using a fuzzy neural network", Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); https://doi.org/10.1117/12.186578

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