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%.

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.

[1] Niu S Z, Wu H, Yu Z F. Total generalized variation minimization based on projection data for low dose CT reconstruction. Nan fang yi ke da xue xue bao Journal of Southern Medical University. (2017) 37(12): 1585-1591.

[2] Zhang H, Han H, Liang Z. Extracting information from previous full-dose CT scan for knowledge-based Bayesian reconstruction of current low-dose CT images. IEEE Transactions on Medical Imaging. (2016) 35(3): 860-870. https://doi.org/10.1109/TMI.2015.2498148

[3] Zhang W, Mao B, Chen X. Self-Adaptive Iterative Step Approach to Noise Reduction in Low-Dose CT Images. SPIE Medical Imaging. (2017) 7(1): 194-196. https://doi.org/10.1109/TMI.2016.2601440

[4] Peter B Noël, Stephan Engels, Thomas Köhler. Evaluation of an iterative model-based CT reconstruction algorithm by intra-patient comparison of standard and ultra-low-dose examinations. Acta Radiologica. (2018) 59(10): 28-41. https://doi.org/10.1177/0284185117752551

[5] Juanjuan Zhao, Guohua Ji, Xiaohong Han. An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging. Frontiers of Computer Science. (2016) 10(1): 55-58. https://doi.org/10.1007/s11704-015-4543-x

[6] Kei Sawada, Akira Tamamori, Kei Hashimoto. A Bayesian Approach to Image Recognition Based on Separable Lattice Hidden Markov Models. Ieice Transactions on Information & Systems. (2016) 99(12): 3119-3131. https://doi.org/10.1587/transinf.2016EDP7112

[7] Hao Zhang, Dong Zeng, Hua Zhang. Applications of nonlocal means algorithm in low‐dose X‐ray CT image processing and reconstruction: A review. Medical Physics. (2017) 44(32): 223-234. https://doi.org/10.1002/mp.12097

[8] Lu Cheng, Yuanke Zhang, Yun Song. Low-Dose CT Image Restoration Based on Adaptive Prior Feature Matching and Nonlocal Means. International Journal of Image and Graphics. (2019) 19(3): 19-25. https://doi.org/10.1142/S0219467819500177

[9] Hailong Rong, Ling Zou, Cuiyun Peng. Adaptive regulation of the weights of REQUEST used to magnetic and inertial measurement unit based on hidden Markov model. Iet Science Measurement Technology. (2018) 12(5): 22-34. https://doi.org/10.1049/iet-smt.2017.0383

[10] Shuo Song, Huabin Chen, Tao Lin. Penetration State Recognition based on the Double-Sound-Sources Characteristic of VPPAW and Hidden Markov Model. Journal of Materials Processing Technology. (2016) 23(4): 33-44. https://doi.org/10.1016/j.jmatprotec.2016.03.002

[11] Huang Z H, Li N, Rao K F. Development of a data-processing method based on Bayesian k -means clustering to discriminate aneugens and clastogens in a high-content micronucleus assay.IEEE Transactions on Medical Imaging. (2018) 37(3): 285-294. https://doi.org/10.1177/0960327117695635

[12] Laila Cochon, Jeffrey Smith, Amado Alejandro Baez. Bayesian comparative assessment of diagnostic accuracy of low-dose CT scan and ultrasonography in the diagnosis of urolithiasis after the application of the STONE score. Emergency Radiology. (2016) 24(2): 1-6. https://doi.org/10.1007/s10140-016-1471-5

[13] Xin Li, Xiaoying Qi, Zetao Chen. Affective Stress Rating Method Based on Improved Hidden Markov Model. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. (2016) 33(3): 533-538.

[14] Amy Ming, Ang Yen, Hsiu si Chen. Bayesian measurement error riven hidden Markov regression model for calibrating the effect of covariates on multistate outcomes: Application to androgenetic alopecia. Statistics in Medicine. (2017) 37(22): 34-43.

[15] S Mukherjee, J Farr, T Merchant. SU-F-I-09: Improvement of Image Registration Using Total-Variation Based Noise Reduction Algorithms for Low-Dose CBCT. Medical Physics. (2016) 43(6): 3388-3388. https://doi.org/10.1118/1.4955837

[16] Han M, Cheng X, Li D. Fast 3D Reconstruction Algorithm of Multi-resolution Cone Beam CT Image Based on Wavelet Transform. Journal of Electronics & Information Technology. (2017) 39(10): 2437-2441.

[17] S Reaungamornrat, T De Silva, A Uneri. TH-CD-206-10: Clinical Application of the MIND Demons Algorithm for Symmetric Diffeomorphic Deformable MR-To-CT Image Registration in Spinal Interventions. Medical Physics. (2016) 43(6): 3885-3885. https://doi.org/10.1118/1.4958191

[18] Alexandr Malusek, Maria Magnusson, Michael Sandborg. A model‐based iterative reconstruction algorithm DIRA using patient‐specific tissue classification via DECT for improved quantitative CT in dose planning. Medical Physics. (2017) 44(53): 11-15. https://doi.org/10.1002/mp.12238

[19] Hongxia Gao, Lan Luo, Yinghao Luo. Improved Stochastic CT Reconstruction Based on Particle Swarm Optimization for Limited-Angle Sparse Projection Data. Acta Optica Sinica. (2018) 38(1): 11-17. https://doi.org/10.3788/AOS201838.0111003

[20] Wu G, Qiu Y J, Wang G R. Map Matching Algorithm Based On Hidden Markov Model and Genetic Algorithm. Journal of Northeastern University. (2017) 38(4): 472-475.

[21] Lu Z B, Wang A M, Tang C T. Multi-Level Relevance Resources Coordinated Scheduling Based on Improved Genetic Algorithm. Beijing Ligong Daxue Xuebao/transaction of Beijing Institute of Technology. (2017) 37(7): 711-716.

[22] Rowley L M, Bradley K M, Boardman P. Optimization of Image Reconstruction for 90Y Selective Internal Radiotherapy on a Lutetium Yttrium Orthosilicate PET/CT System Using a Bayesian Penalized Likelihood Reconstruction Algorithm. Statistics in Medicine. (2017) 58(4): 658-673. https://doi.org/10.2967/jnumed.116.176552

[23] Ayokunle D. Familua, Ling Cheng. A Semi-Hidden Fritchman Markov Modeling of Indoor CENELEC A Narrowband Power Line Noise based on Signal Level Measurements. AEU - International Journal of Electronics and Communications. (2017) 74(11): 21-30. https://doi.org/10.1016/j.aeue.2017.01.015

[24] Chen L, Bi D, Pan J. A Direction of Arrial Estimation Algorithm for Translational Nested Array Besed on Sparse Bayesian Learning. Dianzi Yu Xinxi Xuebao/journal of Electronics & Information Technology. (2018) 40(5): 1173-1180.

[25] Jianning Wu, Haidong Xu, Yun Ling. Human activity pattern recognition based on block sparse Bayesian learning. Journal of Computer Applications. (2016) 23(63): 233-237.

[26] Aviel Atias, Kiril Solovey, Dan Halperin. Effective Metrics for Multi-Robot Motion-Planning. The International Journal of Adwanced Robotics Systems. (2017) 33(53): 14-16. https://doi.org/10.15607/RSS.2017.XIII.022

[27] Sanjay Krishnan, Animesh Garg, Sachin Pati. Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning. International Journal of Adwanced Robotics Systems. (2017) 36(13-14): 27-83. https://doi.org/10.1177/0278364917743319

[28] Zijian Wang, Mac Schwager. Force-Amplifying N-robot Transport System (Force-ANTS) for cooperative planar manipulation without communication. Iternational Journal of Adwanced Robotics Systems. (2016) 35(13): 732-738. https://doi.org/10.1177/0278364916667473

[29] Levine Sergey, Pastor Peter, Krizhevsky Alex. Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection. International Journal of Robotics Research. (2016) 10:82-91.

[30] Schenck Conor, Fox Dieter. Perceiving and Reasoning about Liquids Using Fully Convolutional Networks. International Journal of Robotics Research. (2017) 39:289-318.