A framework for automatic construction of 3D PDM from segmented volumetric neuroradiological data sets

Authors:
Yili Fu;Wenpeng Gao;Yongfei Xiao;Jimin Liu
Affiliations:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, 150080 Harbin, Heilongjiang, China and Bio-X Center, Harbin Institute of Technology, 150080 Harbin, Heilongjiang, China;State Key Laboratory of Robotics and System, Harbin Institute of Technology, 150080 Harbin, Heilongjiang, China and Bio-X Center, Harbin Institute of Technology, 150080 Harbin, Heilongjiang, China;Bio-X Center, Harbin Institute of Technology, 150080 Harbin, Heilongjiang, China;Biomedical Imaging Lab, Agency for Science, Technology and Research, 138671 Singapore, Singapore
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

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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.