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

Hui Tang 1, 2, 3 Jean-Louis Dillenseger 4, * Xu Dong Bao 2, 3 Li Min Luo 2, 3
* Auteur correspondant
1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 LIST - Laboratory of Image Science and Technology [Nanjing]
3 CRIBS - Centre de Recherche en Information Biomédicale sino-français
4 LTSI - Laboratoire Traitement du Signal et de l'Image
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.
keyword : segmentation Gaussian mixture model vectorial image segmentation image classification
Type de document :
Article dans une revue
Computerized Medical Imaging and Graphics, Elsevier, 2009, 33 (8), pp.644-50. 〈10.1016/j.compmedimag.2009.07.001〉
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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〉

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