医学图像融合与联合分割的隐马尔可夫模型
A hidden Markov Model for image fusion and their joint segmentation in medical image computing
摘要：
在这项工作中，我们提出了一个贝叶斯框架，全自动图像融合和联合分割。更具体地说，我们考虑的情况下，我们已经观察到的图像相同的对象通过不同的图像处理或通过不同的光谱带。这项工作的目的是，然后提出一个连贯的方法来结合这些数据集，并获得一个分割的图像，可以被认为是这些意见的融合结果。所提出的方法是基于一个隐马尔可夫模型（HMM）的图像与共同的分割，或等效的，与共同的隐藏的分类标签变量，这是由波茨马尔可夫随机场建模。我们提出了一个适当的马尔可夫链蒙特卡罗（MCMC）算法来实现的方法，并显示一些模拟结果和应用。
英文摘要：
In this work we propose a Bayesian framework for fully automated image fusion and their joint segmentation. More specifically, we consider the case where we have observed images of the same object through different image processes or through different spectral bands. The objective of this work is then to propose a coherent approach to combine these data sets and obtain a segmented image which can be considered as the fusion result of these observations. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently, with common hidden classification label variables which are modeled by the Potts Markov Random Field. We propose an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results and applications.
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