کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
469010 | 698276 | 2010 | 12 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A framework for automatic construction of 3D PDM from segmented volumetric neuroradiological data sets A framework for automatic construction of 3D PDM from segmented volumetric neuroradiological data sets](/preview/png/469010.png)
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.
Journal: Computer Methods and Programs in Biomedicine - Volume 97, Issue 3, March 2010, Pages 199–210