Comparison of supervised pattern recognition techniques and unsupervised methods for MRI segmentation

Laurence P. Clarke; Robert Paul Velthuizen; Lawrence O. Hall; James C. Bezdek; Amine M. Bensaid; Martin L. Silbiger

doi:10.1117/12.59477

1 June 1992 Comparison of supervised pattern recognition techniques and unsupervised methods for MRI segmentation

Laurence P. Clarke, Robert Paul Velthuizen, Lawrence O. Hall, James C. Bezdek, Amine M. Bensaid, Martin L. Silbiger

Proceedings Volume 1652, Medical Imaging VI: Image Processing; (1992) https://doi.org/10.1117/12.59477
Event: Medical Imaging VI, 1992, Newport Beach, CA, United States

Abstract

The use of image intensity based segmentation techniques are proposed to improve MRI contrast and provide greater confidence levels in 3-D visualization of pathology. Pattern recognition methods are proposed using both supervised and unsupervised methods. This paper emphasizes the practical problems in the selection of training data sets for supervised methods that result in instability in segmentation. An unsupervised method, namely fuzzy c- means, that does not require training data sets and produces comparable results is proposed.

Citation Download Citation

Laurence P. Clarke, Robert Paul Velthuizen, Lawrence O. Hall, James C. Bezdek, Amine M. Bensaid, and Martin L. Silbiger "Comparison of supervised pattern recognition techniques and unsupervised methods for MRI segmentation", Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); https://doi.org/10.1117/12.59477

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