A complete and detailed cerebrovascular image segmented from time-of-flight magnetic resonance angiography (TOF-MRA) data is essential for the diagnosis and therapy of the cerebrovascular diseases. In recent years, three-dimensional cerebrovascular segmentation algorithms based on statistical models have been widely used, but the existed methods always perform poorly on stenotic vessels and are not robust enough. In this paper, we propose a parallel cerebrovascular segmentation algorithm based on focused multi-Gaussians model and heterogeneous Markov random field. Specifically, we present a focused multi-Gaussians (FMG) model with local fitting region to model the vascular tissue more accurately and introduce the chaotic oscillation particle swarm optimization (CO-PSO) algorithm to improve the global optimization capability in the parameter estimation. Furthermore, we design a heterogeneous Markov Random Field (MRF) in the three-dimensional neighborhood system to incorporate precise local character of image. Finally, the algorithm has been performed parallel optimization based on GPUs and obtain about 60 times speedup compared to serial execution. The experiments show that the proposed algorithm can produce more detailed segmentation result in shorter time and performs well on the stenotic vessels robustly.

中文翻译：

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基于聚焦多高斯模型和异构马尔可夫随机场的并行脑血管分割算法。

从飞行时间磁共振血管造影（TOF-MRA）数据中分割出来的完整而详细的脑血管图像对于脑血管疾病的诊断和治疗至关重要。近年来，基于统计模型的三维脑血管分割算法已被广泛使用，但是现有的方法在狭窄的血管上总是表现不佳，并且不够鲁棒。在本文中，我们提出了一种基于聚焦多高斯模型和异构马尔可夫随机场的并行脑血管分割算法。特别，我们提出一种具有局部拟合区域的聚焦多高斯（FMG）模型，以更准确地对血管组织进行建模，并引入混沌振荡粒子群优化（CO-PSO）算法，以提高参数估计的全局优化能力。此外，我们在三维邻域系统中设计了一个异构的马尔可夫随机场（MRF），以结合图像的精确局部特征。最后，该算法已基于GPU进行了并行优化，与串行执行相比，可获得约60倍的加速。实验表明，该算法能在更短的时间内产生更详细的分割结果，并且在狭窄血管上表现良好。我们在三维邻域系统中设计了异构Markov随机场（MRF），以融合图像的精确局部特征。最终，该算法已基于GPU进行了并行优化，与串行执行相比，可获得约60倍的加速。实验表明，该算法能在更短的时间内产生更详细的分割结果，并且对狭窄的血管具有良好的鲁棒性。我们在三维邻域系统中设计了异构Markov随机场（MRF），以融合图像的精确局部特征。最后，该算法已基于GPU进行了并行优化，与串行执行相比，可获得约60倍的加速。实验表明，该算法能在更短的时间内产生更详细的分割结果，并且对狭窄的血管具有良好的鲁棒性。