Unsupervised Algorithm for Radiographic Image Segmentation Based on the Gaussian Mixture Model

Type : Article de conférence
Auteur(s) :  F. Mekhalfa, N. Nacereddine, A.B. Goumeidane
Année :  2007
Domaine : Electronique
Conférence: International Conference on Computer as a Tool (EUROCON)
Résumé en PDF :  (résumé en pdf)
Fulltext en PDF :  (.pdf)
Mots clés :  weld defect, radiographic images, image segmentation, Gaussian mixture model, expectation maximization algorithm, fuzzy C-means algorithm

Résumé : 

In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.