Robust MR Image Segmentation Using 3D Partitioned Active Shape Models

Zheen Zhao, E. Teoh
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引用次数: 4

Abstract

A 3D partitioned active shape model (PASM) is proposed in this paper to address the problems of 3D active shape models (ASM) brought by small training sets, which is usually the case in 3D applications. When numbers of training samples are limited, 3D ASMs tend to be restrictive, because the plausible area/allowable region spanned by relatively few eigenvectors cannot capture the full range of shape variability. 3D PASMs overcome this limitation by using a partitioned representation of ASM. Given a point distribution model, the mean mesh is partitioned into a group of small tiles. The statistical priors of tiles are estimated by applying principal component analysis to each tile, and the priors serve as constraints for corresponding tiles during deformations. To avoid the shape inconsistency introduced by the independent estimations between tiles, samples and deformed model points are projected as curves in one hyperspace, instead of point clouds in several hyperspaces. The deformed model points are then fitted into the allowable region of the model using a curve alignment scheme. The experiments on 3D human brain MRIs show that the 3D PASMs segment objects more accurately and are more robust to noise and low contrast in images than two other current active shape models. Furthermore, a study for the PASM's sensitivity to different initializations shows that PASMs perform stable when initialization positions change
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基于三维分割主动形状模型的鲁棒MR图像分割
针对三维应用中训练集小带来的三维活动形状模型问题,提出了一种三维分割活动形状模型(PASM)。当训练样本数量有限时,3D asm往往是限制性的,因为由相对较少的特征向量跨越的合理区域/允许区域不能捕获形状可变性的全部范围。3D ASM通过使用ASM的分区表示克服了这一限制。给定点分布模型,将平均网格划分为一组小块。利用主成分分析方法对每个瓦片进行统计先验估计,作为瓦片变形时的约束条件。为了避免贴图之间的独立估计带来的形状不一致,将样本和变形模型点投影为一个超空间中的曲线,而不是多个超空间中的点云。然后使用曲线对齐方案将变形的模型点拟合到模型的允许区域。三维人脑核磁共振实验表明,与现有的两种活动形状模型相比,该模型能更准确地分割目标,对图像中的噪声和低对比度具有更强的鲁棒性。此外,对不同初始化的灵敏度进行了研究,结果表明,当初始化位置发生变化时,系统表现稳定
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