title: Probabilistic-driven oriented Speckle reducing anisotropic diffusion with application to cardiac ultrasonic images.
abstract: A novel anisotropic diffusion filter is proposed in this work with application to cardiac ultrasonic images. It includes probabilistic models which describe the probability density function (PDF) of tissues and adapts the diffusion tensor to the image iteratively. For this purpose, a preliminary study is performed in order to select the probability models that best fit the stastitical behavior of each tissue class in cardiac ultrasonic images. Then, the parameters of the diffusion tensor are defined taking into account the statistical properties of the image at each voxel. When the structure tensor of the probability of belonging to each tissue is included in the diffusion tensor definition, a better boundaries estimates can be obtained instead of calculating directly the boundaries from the image. This is the main contribution of this work. Additionally, the proposed method follows the statistical properties of the image in each iteration. This is considered as a second contribution since state-of-the-art methods suppose that noise or statistical properties of the image do not change during the filter process.
authors.affiliation: Laboratorio de Procesado de Imagen, Universidad de Valladolid, Spain.
authors.forename: G
authors.initials: G
authors.lastname: Vegas-Sanchez-Ferrero
authors.forename: S
authors.initials: S
authors.lastname: Aja-Fernandez
authors.forename: M
authors.initials: M
authors.lastname: Martin-Fernandez
authors.forename: A F
authors.initials: AF
authors.lastname: Frangi
authors.forename: C
authors.initials: C
authors.lastname: Palencia
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D016477:Artifacts; D003627:Data Interpretation, Statistical; D004452:Echocardiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity