COMPUTER TOMOGRAPHY IMAGES QUALITY ASSESSMENT
DOI:
https://doi.org/10.18372/1990-5548.63.14495Keywords:
Artifacts, medical imaging, metrics, subjective measure, Gaussian filter, medianAbstract
The digital images quality estimating methods that are distorted by artifacts are considered. The most common types of artifacts are artifacts due to increased beam stiffness, the effect of partial volume filling, arbitrary patient movements during examination, etc. Quantitative and subjective measures are used to evaluate the quality of digital images. A comprehensive evaluation of the quality of tomographic images is proposed, based on the combination of the two above metrics, which will allow to better evaluate the quality of the tomographic image and to automate the evaluation process itself. A technique for evaluating the quality of tomographic images has been developed, which will allow for a more accurate diagnosis by eliminating the subjectivity of the physician.References
Medical Informatics: [textbook for medical students], edited by V.G. Knigavko. Kharkiv: KHNMU, 2015, 288 p. (in Ukrainian)
M. Ya. Marusina, A. O. Kaznacheeva, Modern types of tomography. Training allowance. St. Petersburg: St. Petersburg State University ITMO, 2006, 132 p.
O Watzke, “A pragmatic approach to metal artifact reduction in CT: merging of metal artifact reduced images,” European Radiology, 14: 849–856, 2004. https://doi.org/10.1007/s00330-004-2263-y
Wang X., Tian B., Liang C., Shi D. Blind Image Quality Assessment for Measuring Image Blur // Congress on Image and Signal 2008 Congress on Image and Signal Processing, 2008. https://doi.org/10.1109/CISP.2008.371
X. Li and J. Cai, “Robust transmission of JPEG2000 encoded images over packet loss channels,” Multimedia and Expo, 2007 IEEE International Conference on, IEEE, 2007, pp. 947–950. https://doi.org/10.1109/ICME.2007.4284808
N. Thomos, N. V. Boulgouris, and M. G. Strintzis, “Optimized transmission of JPEG2000 streams over wireless channels,” IEEE Transactions on image processing, vol. 15no. 1, 2006, pp. 54–67. https://doi.org/10.1109/TIP.2005.860338
A. Hore and D. Ziou, “Image quality metrics: PSNR vs. SSIM,” Pattern recognition (icpr), 2010 20th international conference on. IEEE, 2010, pp. 2366–2369. https://doi.org/10.1109/ICPR.2010.579
A. G. Volkov and O. V. Krasnopolsky, Additional opportunities to reduce errors in the radiological diagnosis of frontal sinusitis: The collection of materials of the medical scientific-practical conference dedicated to the 80th anniversary of the city hospital №1. Rostov-on-Don, 2012. (in Russian)
L. S. Chow and R. Paramesran, “Review of medical image quality assessment,” Biomed. Signal Process. Control, vol. 27, pp. 145–154, 2016. https://doi.org/10.1016/j.bspc.2016.02.006
X. He and S. Park, “Model observes in medical imaging research,” Theranostics, vol. 3, no. 10, pp. 774–786, 2015. https://doi.org/10.7150/thno.5138
A. Mason, N. Murtha, J. Rioux, S. Clarke, C. Bowen, and S. Beyea, “Pharmocokinetic Parameter Accuracy Correlates with Image Quality Metrics in Flexible Temporal Resolution DCE-MRI,” in Proc. Intl. Soc. Mag. Reson. Med., 2019, p. 535.
H. Jeelani, J. Martin, F, Vasquez, M. Salerno, and D. S. Weller, “Image Quality Affercts Deep Learning Reconstruction of MRI,” in 2018 IEEE 15th International Symposium on Biomedical Imaging ISBI, 2018, pp. 357–360. https://doi.org/10.1109/ISBI.2018.8363592
A. S. Chandhari et al, “Super-resolution musculoskeletal MRI using deep learning,” Magn. Reson. Med., vol. 80, no. 5, pp. 2139–2154, 2018. https://doi.org/10.1002/mrm.27178
B. A. Duffy, “Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion,” Med Imaging with Deep Learn., no. Midl 2018, pp. 1–8, 2018.
H. Zheng et al., “Multi-Contrast Brain CT Image Super-Resolution with Gradient-Guided Edge Enchancement,” IEEE Access, vol. 6, pp. 57856–57867, 2018. https://doi.org/10.1109/ACCESS.2018.2873484
Downloads
How to Cite
Issue
Section
License
The scientific journal “Electronics and control systems” adheres to the principles of Open Access and provides free, immediate, and permanent access to all published materials without financial, technical, or legal barriers for readers.
All articles are published in Open Access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Copyright
Authors who publish their works in the journal “Electronics and control systems”:
-
retain the copyright to their publications;
-
grant the journal the right of first publication of the article;
-
agree to the distribution of their materials under the CC BY 4.0 license;
-
have the right to reuse, archive, and distribute their works (including in institutional and subject repositories), provided that proper reference is made to the original publication in the journal.