Vol. 1 No. 9 (2023), Articles
Vol. 1 No. 9 (2023)

Color-aware digital image segmentation procedure as a tool for studying fatty liver disease

Articles
Published 2023-09-30
Boboyorov Sardor Uchqun o‘g‘li
Tashkent Medical Academy Termiz branch, Uzbekistan
Sinelnikova Tetiana
Department of Informatics, Kharkiv National University of Radio Electronics, Ukraine
Zeleniy Oleksandr
Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukraine
Lyashenko Vyacheslav
Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukraine
PDF
DOI

Keywords

Segmentation, Diagnostics, Analysis, Liver, Fatty disease, Medical image, Color space

How to Cite

Color-aware digital image segmentation procedure as a tool for studying fatty liver disease. (2023). Journal of Universal Science Research, 1(9), 431-441. http://universalpublishings.com/index.php/jusr/article/view/2070
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Abstract

Digital medical images are one of the sources of information for timely prevention of possible diseases. In this case, color medical images are widely used. One example of such use is in the diagnosis of fatty liver disease. Based on this, the paper considers the possibility of studying fatty liver disease based on digital image segmentation taking into account color. The results of digital processing are presented for real images depicting lesions of fatty liver disease.

PDF
DOI

References

Bercovich, E., & Javitt, M. C. (1995). Medical Imaging: From Roentgen to the Digital Revolution, and Beyond. Rambam Maimonides Med J 2018; 9 (4): e0034. Review.

Saha, P. K., Strand, R., & Borgefors, G. (2015). Digital topology and geometry in medical imaging: a survey. IEEE transactions on medical imaging, 34(9), 1940-1964.

Orobinskyi, P., & et al.. (2020). Comparative Characteristics of Filtration Methods in the Processing of Medical Images. American Journal of Engineering Research, 9(4), 20-25.

Lyashenko, V., Kobylin, O., & Ahmad, M. A. (2014). General methodology for implementation of image normalization procedure using its wavelet transform. International Journal of Science and Research (IJSR), 3(11), 2870-2877.

Baranova, V., & et al.. (2019, October). Stochastic Frontier Analysis and Wavelet Ideology in the Study of Emergence of Threats in the Financial Markets. In 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T) (pp. 341-344). IEEE.

Слюніна, Т. Л., Бережний, Є. Б., & Ляшенко, В. В. (2007). Розвиток вітчизняної мережі банківських установ: особливості та регіональні аспекти. Вісник ХНУ ім. В. Н. Каразіна. Економічна серія, 755. 84–88.

Lyubchenko, V., & et al.. (2016). Digital image processing techniques for detection and diagnosis of fish diseases. International Journal of Advanced Research in Computer Science and Software Engineering, 6(7), 79-83.

Al-Sharo, Y. M., & et al.. (2021). Neural Networks As A Tool For Pattern Recognition of Fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.

Bankman, I. (Ed.). (2008). Handbook of medical image processing and analysis. Elsevier.

Semmlow, J. L. (2008). Biosignal and medical image processing. CRC press.

Sharma, N., & Aggarwal, L. M. (2010). Automated medical image segmentation techniques. Journal of medical physics/Association of Medical Physicists of India, 35(1), 3.

Ramesh, K. K. D., & et al.. (2021). A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 7(27), e6-e6.

Boboyorov Sardor Uchqun o‘g‘li, Lyubchenko Valentin, & Lyashenko Vyacheslav. (2023). Image Processing Techniques as a Tool for the Analysis of Liver Diseases. Journal of Universal Science Research, 1(8), 223–233.

Boboyorov Sardor Uchqun o‘g‘li, Lyubchenko Valentin, & Lyashenko Vyacheslav. (2023). Pre-processing of digital images to improve the efficiency of liver fat analysis. Multidisciplinary Journal of Science and Technology, 3(1), 107–114.

Boboyorov Sardor Uchqun o‘g‘li, Tanianskyi Oleksii, Belova Nataliya, & Lyashenko Vyacheslav. (2023). Contrasting as a Method of Processing Medical Images in the Study of Fatty Liver Disease. Journal of Universal Science Research, 1(9), 29–39.

Masood, S., & et al.. (2015). A survey on medical image segmentation. Current Medical Imaging, 11(1), 3-14.

Malhotra, P., Gupta, S., Koundal, D., Zaguia, A., & Enbeyle, W. (2022). Deep neural networks for medical image segmentation. Journal of Healthcare Engineering, 2022.

Tahseen A. J. A., & et al.. (2023). Binarization Methods in Multimedia Systems when Recognizing License Plates of Cars. International Journal of Academic Engineering Research (IJAER), 7(2), 1-9.

Lee, L. K., Liew, S. C., & Thong, W. J. (2015). A review of image segmentation methodologies in medical image. In Advanced Computer and Communication Engineering Technology: Proceedings of the 1st International Conference on Communication and Computer Engineering (pp. 1069-1080). Springer International Publishing.

Salem, M. A. M., & et al.. (2017). Recent survey on medical image segmentation. In Handbook of Research on Machine Learning Innovations and Trends (pp. 424-464). IGI global.

Avalos, O., & et al.. (2021). An accurate Cluster chaotic optimization approach for digital medical image segmentation. Neural Computing and Applications, 33, 10057-10091.

Chae, S. H., Moon, H. M., Chung, Y., Shin, J., & Pan, S. B. (2016). Automatic lung segmentation for large-scale medical image management. Multimedia Tools and Applications, 75, 15347-15363.

Chang, P. L., & Teng, W. G. (2007, June). Exploiting the self-organizing map for medical image segmentation. In Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07) (pp. 281-288). IEEE.

Norouzi, A., & at al.. (2014). Medical image segmentation methods, algorithms, and applications. IETE Technical Review, 31(3), 199-213.

Tyagi, P., & et al.. (2018, February). Performance comparison and analysis of medical image segmentation techniques. In 2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) (pp. 1-6). IEEE.

Reddy, A. S., & Reddy, P. C. (2019). A hybrid K-means algorithm improving low-density map-based medical image segmentation with density modification. International Journal of Biomedical Engineering and Technology, 31(2), 176-192.

Kobylin, O., & Lyashenko, V. (2020). Time Series Clustering Based on the K-Means Algorithm. Journal La Multiapp, 1(3), 1-7.

Abdulla, S. H., Sagheer, A. M., & Veisi, H. (2022). Breast cancer segmentation using K-means clustering and optimized region-growing technique. Bulletin of Electrical Engineering and Informatics, 11(1), 158-167.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.