The nearest neighbor rule has previously been shown to be the most reliable method for segmentation of at least a certain range of magnetic resonance images compared with other supervised learning techniques. A nearest neighbor classifier may require long computing time and large memory space if the number of prototypes used is large. The authors present a method for image segmentation using optimized nearest neighbor classifiers. In the method only a very small number of prototypes are generated from training samples using an unsupervised learning method. The prototypes are then optimized using a neural network based on supervised learning. The optimized nearest neighbor classifier is robust in performance for image segmentation and very efficient for practical implementation.<>
Magnetic resonance image segmentation using optimized nearest neighbor classifiers
Proceedings of 1st International Conference on Image Processing ; 3 ; 49-52 vol.3
01.01.1994
376416 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Magnetic Resonance Image Segmentation Using Optimized Nearest Neighbor Classifiers
British Library Conference Proceedings | 1994
|British Library Conference Proceedings | 1994
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