Segmentation of MR and CT images by using a quantiser neural network

Dokur Z., Olmez T.

NEURAL COMPUTING & APPLICATIONS, vol.11, pp.168-177, 2003 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11
  • Publication Date: 2003
  • Doi Number: 10.1007/s00521-003-0355-2
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.168-177
  • Istanbul Technical University Affiliated: Yes


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.