A Novel 3D Partitioned Active Shape Model for Segmentation of Brain MR Images
A Novel 3D Partitioned Active Shape Model for Segmentation of Brain MR Images
复制标题
用于大脑 MR 图像分割的新型 3D 分区活动形状模型
DOI：
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发表时间：
2005
期刊：
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通讯作者：
E. Teoh
中科院分区：
文献类型：
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作者：
Zheen Zhao;S. Aylward;E. Teoh
针对三维活动形状模型存在的问题，提出了一种三维分区活动形状模型（PASM）。当训练集很小时。在3D分割中，3D asm往往是限制性的。这是因为由相对较少的特征向量跨越的允许区域不能捕获形状可变性的全部范围。3D PASM通过使用ASM的分区表示克服了这一限制。给定点分布模型（PDM），将平均网格划分为一组小块。为了约束瓦片的变形，采用主成分分析对瓦片进行统计先验估计。为了避免贴图之间的形状不一致，训练样本在一个超空间中被投影为曲线，而不是在多个超空间中被投影为点云。然后使用曲线对齐方案将变形点拟合到模型的允许区域。通过对三维人脑核磁共振成像的实验表明，在训练样本数量有限的情况下，与三维asm和三维分层asm相比，三维PASMs的分割效果明显改善。
A 3D Partitioned Active Shape Model (PASM) is proposed in this paper to address the problems of the 3D Active Shape Models (ASM). When training sets are small. It is usually the case in 3D segmentation, 3D ASMs tend to be restrictive. This is because the allowable region spanned by relatively few eigenvectors cannot capture the full range of shape variability. The 3D PASM overcomes this limitation by using a partitioned representation of the ASM. Given a Point Distribution Model (PDM), the mean mesh is partitioned into a group of small tiles. In order to constrain deformation of tiles, the statistical priors of tiles are estimated by applying Principal Component Analysis to each tile. To avoid the inconsistency of shapes between tiles, training samples are projected as curves in one hyperspace instead of point clouds in several hyperspaces. The deformed points are then fitted into the allowable region of the model by using a curve alignment scheme. The experiments on 3D human brain MRIs show that when the numbers of the training samples are limited, the 3D PASMs significantly improve the segmentation results as compared to 3D ASMs and 3D Hierarchical ASMs.