A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model.

Abstract : The CT uroscan consists of three to four time-spaced acquisitions of the same patient. After registration of these acquisitions, the data forms a volume in which each voxel contains a vector of elements corresponding to the information of the CT uroscan acquisitions. In this paper we will present a segmentation tool in order to differentiate the anatomical structures within the vectorial volume. Because of the partial volume effect (PVE), soft segmentation is better suited because it allows regions or classes to overlap. Gaussian mixture model is often used in statistical classifier to realize soft segmentation by getting classes probability distributions. But this model relies only on the intensity distributions, which will lead a misclassification on the boundaries and on inhomogeneous regions with noise. In order to solve this problem, a neighborhood weighted Gaussian mixture model is proposed in this paper. Expectation maximization algorithm is used as optimization method. The experiments demonstrate that the proposed method can get a better classification result and is less affected by the noise.
Liste complète des métadonnées

Littérature citée [11 références]  Voir  Masquer  Télécharger

http://www.hal.inserm.fr/inserm-00411983
Contributeur : Jean-Louis Dillenseger <>
Soumis le : lundi 31 août 2009 - 12:44:25
Dernière modification le : mercredi 16 mai 2018 - 11:23:40
Document(s) archivé(s) le : mardi 15 juin 2010 - 22:57:29

Fichier

compMedImGraph09.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Hui Tang, Jean-Louis Dillenseger, Xu Dong Bao, Li Min Luo. A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model.. Computerized Medical Imaging and Graphics, Elsevier, 2009, 33 (8), pp.644-50. 〈10.1016/j.compmedimag.2009.07.001〉. 〈inserm-00411983〉

Partager

Métriques

Consultations de la notice

715

Téléchargements de fichiers

249