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 dil | İngilizce |
---|---|
Sayfa (başlangıç-bitiş) | 168-177 |
Sayfa sayısı | 10 |
Dergi | Neural Computing and Applications |
Hacim | 11 |
Basın numarası | 3-4 |
DOI'lar | |
Yayın durumu | Yayınlandı - May 2003 |