Magnetic resonance image segmentation using optimized nearest neighbor classifiers

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.