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

Abstract : The CT uroscan contains three to four time-spaced acquisitions of the same patient. Registration of these acquisitions forms a vectorial volume, which contains a more complete anatomical information. In order to outline the anatomical structures, multi-dimensional classification is necessary for analyzing this vectorial volume. Because of the partial volume effect (PVE), probability distributions are assigned to the different material types within this vectorial volume instead of a definite material distribution. Gaussian mixture model is often used in probability classification problems to model such distributions, but it relies only on the intensity distributions, which will lead a misclassification on the boundaries and inhomogeneous regions with noises. 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 less affected by the noise.
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https://www.hal.inserm.fr/inserm-00316890
Contributor : Jean-Louis Dillenseger <>
Submitted on : Friday, September 5, 2008 - 3:42:26 PM
Last modification on : Tuesday, September 3, 2019 - 6:02:02 PM
Long-term archiving on: : Thursday, June 3, 2010 - 6:28:12 PM

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  • HAL Id : inserm-00316890, version 1
  • PUBMED : 19163066

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Hui Tang, Jean-Louis Dillenseger, Limin Luo. A vectorial image classification method based on neighborhood weighted Gaussian mixture model.. Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2008, 1, pp.1922-5. ⟨inserm-00316890⟩

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