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首页> 外文期刊>Medical image analysis >Segmentation of brain magnetic resonance angiography images based on MAP-MRF with multi-pattern neighborhood system and approximation of regularization coefficient
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Segmentation of brain magnetic resonance angiography images based on MAP-MRF with multi-pattern neighborhood system and approximation of regularization coefficient

机译：基于MAP-MRF的脑磁共振血管造影图像的分割与多模式邻域系统和正则化系数的近似

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摘要

Existing maximum a posteriori probability and Markov random field (MRF) models have limitations associated with: (1) the ordinary neighborhood system being unable to differentiate subtle changes due to several-to-one correspondence within the neighborhood; and (2) difficulty finding an appropriate parameter to balance between the spatial context and the data likelihood. Aiming at overcoming the limitations and applications to segmentation of cerebral vessels from magnetic resonance angiography images, we have proposed (1) a multi-pattern neighborhood system and corresponding energy equation to enable the MRF model for segmenting fine cerebral vessels with complicated context; and (2) an iterative approximation algorithm based on the maximum pseudo-likelihood and the space coding mode for the automatic parameter estimation of high level model of MRF. In the implementation, two computational strategies have been employed to speed up: the candidate space of cerebral vessels based on a threshold of the response to multi-scale filtering, and parallel computation of major equations. Three phantoms simulating segmentation challenges of vessels have been devised to quantitatively validate the algorithm. In addition, 10 three-dimensional clinical data sets have been used to validate the algorithm qualitatively. It has been shown that the proposed method could yield smaller error, improve the spatial resolution of MRF model, and better balance the smoothing and data likelihood than the traditional trial-and-error estimation. Comparative studies have shown that the proposed method is better than the 3 segmentation algorithms (Hassouna et al., 2006; Hao et al, 2008; Gao et al., 2011) in terms of segmentation accuracy, robustness to noise and varying curvatures as well as radii.
机译：现有的最大后验概率和马尔可夫随机字段（MRF）模型具有与：（1）普通邻域系统无法区分细微的变化，由于邻域内的几个对应关系; （2）难以在空间上下文和数据可能性之间找到适当的参数来平衡。旨在克服来自磁共振血管造影图像的脑血管分割的局限性和应用，我们已经提出（1）多图案邻域系统和相应的能量方程，以使MRF模型用于分割具有复杂背景的细脑血管; （2）基于最大伪可能性的迭代近似算法和MRF的高级模型自动参数估计的空间编码模式。在实施中，已经采用了两种计算策略来加速：基于对多尺度滤波的响应的阈值以及主要方程的并行计算的阈值。三个幻影模拟船舶的分割挑战已经设计为定量验证算法。此外，已经使用10个三维临床数据集来定性验证算法。已经表明，所提出的方法可以产生较小的误差，提高MRF模型的空间分辨率，并且更好地平衡比传统的试验和误差估计更好地平衡平滑和数据似然。比较研究表明，该方法优于3分割算法（Hassouna等，2006; Hao等，2008; Gao等，2011）在分割准确性方面，噪音和不同曲率的鲁棒性以及不同的曲率作为radii。

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