International Journal of Multimedia Computing, 2023, 4(1); doi: 10.38007/IJMC.2023.040104.
Department of Information Engineering, Heilongjiang International University, Heilongjiang, China
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 images, the improved hidden markov algorithm based on bayes plays an obvious role in changing the resolution of low-dose CT images. Experimental data show that compared with other denoising methods, the peak signal-to-noise ratio of the de-noised image can be improved, the detailed features of the image can be retained better, the visual effect can be improved by 46.78%, and the correct segmentation rate can reach 95.75%.
Improved Bayes, Improved Hidden Markov Algorithm, Low-dose CT Image, Visual Effects, Image Quality
Xiangru Hou.Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images. International Journal of Multimedia Computing (2023), Vol. 4, Issue 1: 54-68. https://doi.org/10.38007/IJMC.2023.040104.
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