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.<>


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    Title :

    Magnetic resonance image segmentation using optimized nearest neighbor classifiers


    Contributors:
    Hong Yan (author) / Jingtong Mao (author) / Yan Zhu (author) / Chen, B. (author)


    Publication date :

    1994-01-01


    Size :

    376416 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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