Cerebrovascular segmentation from TOF using stochastic models
Cerebrovascular segmentation from TOF using stochastic models
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DOI：
10.1016/j.media.2004.11.009
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发表时间：
2006-02-01
影响因子：
10.9
通讯作者：
Moriarty, T
中科院分区：
文献类型：
--
作者：
Hassouna, MS;Farag, AA;Moriarty, T
在本文中，我们提出了一种自动统计的方法提取三维血管的飞行时间（TOF）磁共振血管造影（MRA）数据。数据集的体素被分类为血管或背景噪声。观测的体积数据由两个随机过程建模。低层处理表征数据的强度分布，而高层处理表征它们在相邻体素之间的统计依赖性。背景信号的低电平过程由一个瑞利分布和两个正态分布的有限混合来建模，而血管由一个正态分布来建模。使用期望最大化（EM）算法估计低水平过程的参数。由于EM的收敛性对模型参数的初始估计敏感，因此提供了一种基于直方图分析的参数初始化自动方法。为了提高所提出的低级别模型，特别是在显着的血管信号丢失的区域中实现的分割的质量，高级别的过程被建模为马尔可夫随机场（MRF）。由于MRF对边缘敏感，并且颅内血管约占颅内体积的5%，因此2D MRF将破坏大多数中小型血管。因此，为了减少这种限制，我们采用了三维MRF，其参数估计使用最大伪似然估计（MPLE），它收敛到大格点下的真实似然。我们提出的模型表现出良好的拟合临床数据，并在不同的合成血管体模和从两个不同的MRI扫描仪采集的几个2D/3D TOF数据集上进行了广泛的测试。实验结果表明，该模型提供了良好的分割质量，并能够描绘血管下降到3体素直径。(c)2005 Elsevier B. V.保留所有权利。
In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either blood vessels or background noise. The observed volume data is modeled by two stochastic processes. The low level process characterizes the intensity distribution of the data, while the high level process characterizes their statistical dependence among neighboring voxels. The low level process of the background signal is modeled by a finite mixture of one Rayleigh and two normal distributions, while the blood vessels are modeled by one normal distribution. The parameters of the low level process are estimated using the expectation maximization (EM) algorithm. Since the convergence of the EM is sensitive to the initial estimate of the model parameters, an automatic method for parameter initialization, based on histogram analysis, is provided. To improve the quality of segmentation achieved by the proposed low level model especially in the regions of significantly vascular signal loss, the high level process is modeled as a Markov random field (MRF). Since MRF is sensitive to edges and the intracranial vessels represent roughly 5% of the intracranial volume, 2D MRF will destroy most of the small and medium sized vessels. Therefore, to reduce this limitation, we employed 3D MRF, whose parameters are estimated using the maximum pseudo likelihood estimator (MPLE), which converges to the true likelihood under large lattice. Our proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several 2D/3D TOF datasets acquired from two different MRI scanners. Experimental results showed that the proposed model provides good quality of segmentation and is capable of delineating vessels down to 3 voxel diameters. (c) 2005 Elsevier B.V. All rights reserved.