title: A framework for automatic construction of 3D PDM from segmented volumetric neuroradiological data sets.
abstract: 3D point distribution model (PDM) of subcortical structures can be applied in medical image analysis by providing priori-knowledge. However, accurate shape representation and point correspondence are still challenging for building 3D PDM. This paper presents a novel framework for the automated construction of 3D PDMs from a set of segmented volumetric images. First, a template shape is generated according to the spatial overlap. Then the corresponding landmarks among shapes are automatically identified by a novel hierarchical global-to-local approach, which combines iterative closest point based global registration and active surface model based local deformation to transform the template shape to all other shapes. Finally, a 3D PDM is constructed. Experiment results on four subcortical structures show that the proposed method is able to construct 3D PDMs with a high quality in compactness, generalization and specificity, and more efficient and effective than the state-of-art methods such as MDL and SPHARM.
authors.affiliation: State Key Laboratory of Robotics and System, Harbin Institute of Technology, 150080 Harbin, Heilongjiang, China.
authors.forename: Yili
authors.initials: Y
authors.lastname: Fu
authors.forename: Wenpeng
authors.initials: W
authors.lastname: Gao
authors.forename: Yongfei
authors.initials: Y
authors.lastname: Xiao
authors.forename: Jimin
authors.initials: J
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001331:Automation; D021621:Imaging, Three-Dimensional