title: 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.
authors.affiliation: Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, 210096 Nanjing, China. corinna@seu.edu.cn
authors.forename: Hui
authors.initials: H
authors.lastname: Tang
authors.forename: Jean-Louis
authors.initials: JL
authors.lastname: Dillenseger
authors.forename: Xu Dong
authors.initials: XD
authors.lastname: Bao
authors.forename: Li Min
authors.initials: LM
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007668:Kidney / Q000000981:diagnostic imaging*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*; D014567:Urography / Q000379:methods*