Segmentation of MR and CT Images by Using a Quantiser Neural Network |
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Authors: | Zümray Dokur Tamer Ölmez |
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Affiliation: | (1) Department of Electronics and Communication Engineering, Istanbul Technical University, 80626 Maslak, Istanbul, Turkey |
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Abstract: | A Quantiser Neural Network (QNN) is proposed for the segmentation of MR and CT images. Elements of a feature vector are formed by image intensities at one neighbourhood of the pixel of interest. QNN is a novel neural network structure, which is trained by genetic algorithms. Each node in the first layer of the QNN forms a hyperplane (HP) in the input space. There is a constraint on the HPs in a QNN. The HP is represented by only one parameter in d-dimensional input space. Genetic algorithms are used to find the optimum values of the parameters which represent these nodes. The novel neural network is comparatively examined with a multilayer perceptron and a Kohonen network for the segmentation of MR and CT head images. It is observed that the QNN gives the best classification performance with fewer nodes after a short training time. |
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Keywords: | Classification Genetic algorithms Multi-layer perceptron Neural networks Partitioning of feature space Segmentation of MR and CT images |
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