基于贝叶斯的低剂量CT图像改进隐马尔可夫算法

Xiangru Hou
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引用次数: 0

摘要

:为了减少患者在CT扫描时的辐射暴露,产生了低剂量的CT图像,但缺点是图像质量降低。贝叶斯最大后验概率估计(Bayesian MAP)方法是一种实用的统计方法,可以从受噪声污染的图像细节系数中估计出原始的与噪声无关的系数。本文旨在研究基于贝叶斯的改进隐马尔可夫算法在低剂量CT图像中的应用,这是近年来CT研究的热点。本实验首先在实验范围内对hmrf-em、eHMRF算法和hmrf-msa-em算法进行数理统计分析,并通过不同系数对比该算法的优越性。采用朴素贝叶斯算法和基于贝叶斯的改进隐马尔可夫算法对重数据统计方法进行分类和统计分析。并采用单变量法比较使用基于贝叶斯的改进隐马尔可夫算法在低剂量CT图像成像中是否存在不同的变化,以及变化的程度。实验数据表明,改进的基于贝叶斯的隐马尔可夫算法在不同噪声水平下获得了更高的Jaccard、Dice和CCR值。改进的基于贝叶斯的隐马尔可夫算法比朴素贝叶斯算法获得的低剂量CT图像更清晰。在各种医学领域
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Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images
: In order to reduce the radiation exposure of patients during CT scan, low-dose CT images were produced, but the disadvantage was that the image quality was reduced. The Bayesian maximum posterior probability estimation (Bayesian MAP) method is an applied statistical method that can estimate the original noise-independent coefficients from the noise-contaminated image detail coefficients. This paper aims to study the application of bayesian based improved hidden markov algorithm in low-dose CT images, which has become the focus of CT research in recent years. In this experiment, hmrf-em, eHMRF algorithm and hmrf-msa-em algorithm were firstly analyzed by mathematical statistics within the experimental scope, and the superiority of this algorithm was compared by looking at different coefficients. The classification and statistical analysis of the re-data statistical method were carried out by using the naive bayesian algorithm and the improved hidden markov algorithm based on bayes. And the use of a single variable method to compare the use of bayesian based improved hidden markov algorithm in the low-dose CT image imaging whether there are different changes, and the degree of change. Experimental data show that the improved hidden markov algorithm based on bayes achieves higher values of Jaccard, Dice and CCR at different noise levels. The improved hidden markov algorithm based on bayes is clearer than the low-dose CT images obtained by the naive bayes algorithm. In various medical
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CiteScore
1.40
自引率
16.70%
发文量
23
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