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Vascular Extraction Using MRA Statistics and Gradient Information

机译：使用MRA统计信息和梯度信息提取血管

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摘要

Brain vessel segmentation is a fundamental component of cerebral disease screening systems. However, detecting vessels is still a challenging task owing to their complex appearance and thinning geometry as well as the contrast decrease from the root of the vessel to its thin branches. We present a method for segmentation of the vasculature in Magnetic Resonance Angiography (MRA) images. First, we apply volume projection, 2D segmentation, and back-projection procedures for first stage of background subtraction and vessel reservation. Those labeled as background or vessel voxels are excluded from consideration in later computation. Second, stochastic expectation maximization algorithm (SEM) is used to estimate the probability density function (PDF) of the remaining voxels, which are assumed to be mixture of one Rayleigh and two Gaussian distributions. These voxels can then be classified into background, middle region, or vascular structure. Third, we adapt the K-means method which is based on the gradient of remaining voxels to effectively detect true positives around boundaries of vessels. Experimental results on clinical cerebral data demonstrate that using gradient information as a further step improves the mixture model based segmentation of cerebral vasculature, in particular segmentation of the low contrast vasculature.
机译：脑血管分割是脑疾病筛查系统的基本组成部分。但是，由于其复杂的外观和变薄的几何形状以及从容器根部到其细枝的对比度下降，检测容器仍然是一项艰巨的任务。我们提出了一种在磁共振血管造影（MRA）图像中分割脉管的方法。首先，我们将体积投影，二维分割和反投影程序应用于背景减影和血管保留的第一阶段。标记为背景或血管体素的对象将不在以后的计算中考虑。其次，使用随机期望最大化算法（SEM）估计剩余体素的概率密度函数（PDF），这些体素被假定为一个瑞利分布和两个高斯分布的混合体。然后可以将这些体素分类为背景，中间区域或血管结构。第三，我们采用基于剩余体素梯度的K均值方法来有效检测血管边界周围的阳性。临床大脑数据的实验结果表明，将梯度信息用作下一步可以改善基于混合模型的脑血管系统分割，特别是低对比度血管系统的分割。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第2期|6131325.1-6131325.8|共8页
  • 作者

    Zhao Shifeng; Tian Yun; Wang Xuesong; Xu Pengfei; Deng Qinqiong; Zhou Mingquan;

  • 作者单位

    Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China;

    Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China;

    Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China;

    Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China;

    Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China;

    Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China;

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