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Path Integral Based Contour Extraction Method Using Multilevel Metropolis Sampling

机译:多层都会抽样的基于路径积分的轮廓提取方法

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In this paper we proposed a novel path integral method using multilevel Metropolis sampling to extract the contours of interested objects of medical images, which is a quantum statistical approach inspired by the essential characteristics of quantum. The implemented multilevel Metropolis sampling is a fast approach to the sampling problem and can be used to sample any distribution function and calculate all actions for the movements of quantum particle towards the position with high probability density likely to be accepted. Comparing with traditional deformable models based on classical mechanics which are sensitive to the initialization of contour and difficult to adapt topology changes of the contour, our work has the capability to break through the limitations of deformable models and makes the boundary extraction in high noise and low contrast of medical images possible. Experiments were performed with synthetic and medical images and the feasibility and potential of our method was demonstrated.
机译:在本文中,我们提出了一种新的路径积分方法,该方法使用多级Metropolis采样来提取医学图像感兴趣对象的轮廓,这是一种受量子本质特征启发的量子统计方法。已实施的多级Metropolis采样是解决采样问题的快速方法,可用于采样任何分布函数并计算量子粒子向可能被接受的高概率密度位置移动的所有动作。与基于经典力学的传统变形模型相比,该模型对轮廓的初始化敏感并且难以适应轮廓的拓扑变化,因此我们的工作能够突破变形模型的局限性,并在高噪声和低噪声下进行边界提取。医学图像的对比可能。用合成图像和医学图像进行了实验,证明了我们方法的可行性和潜力。

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