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


Dokur Z. , Olmez T.

NEURAL COMPUTING & APPLICATIONS, cilt.11, ss.168-177, 2003 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 11
  • Basım Tarihi: 2003
  • Doi Numarası: 10.1007/s00521-003-0355-2
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Sayfa Sayıları: ss.168-177

Özet

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.