Radiographic Image Segmentation Based on Gaussian Mixture Model

Type : Article de conférence
Auteur(s) :  F. Mekhalfa, N. Nacereddine, A. B. Goumeïdane
Année :  2007
Domaine : Electronique
Conférence: The Eighth International Symposium On Programming and Systems (ISPS)
Lieu de la conférence: 
Résumé en PDF :  (résumé en pdf)
Fulltext en PDF :  (.pdf)
Mots clés :  — expectation maximization algorithm, fuzzy C-means algorithm, Gaussian mixture model, image segmentation, radiographic images, weld defect

Résumé : 

In this work, we propose to use an image segmentation method based on Gaussian mixture model. The observed image is considered as a mixture of multivariate densities and the mixture parameters are estimated by the expectation maximization (EM) algorithm. The segmentation is completed by clustering each pixel into a component according to the maximum likelihood (ML) estimation. This method has been applied to a variety of radiographic images of weld defects and satisfactory segmentation results have been reported.