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

Zümray Dokur*, Tamer Ölmez

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

3 Atıf (Scopus)

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)168-177
Sayfa sayısı10
DergiNeural Computing and Applications
Hacim11
Basın numarası3-4
DOI'lar
Yayın durumuYayınlandı - May 2003

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