Contrasting as a Method of Processing Medical Images in the Study of Fatty Liver Disease
- 1. Tashkent Medical Academy Termiz branch, Uzbekistan
- 2. Department of Informatics, Kharkiv National University of Radio Electronics, Ukraine
- 3. Department of Media Systems and Technology, Kharkiv National University of Radio Electronics, Ukraine
Description
Diagnosis of diseases is one of the priorities in the development of modern medicine. Various data sources can be used for these purposes. Among such sources, medical images should be singled out, which reflect the microcosm of the phenomena under study. The study of medical images is possible based on the use of various image processing techniques. These techniques allow you to prepare the input image for research and perform the necessary analysis. One of the image processing techniques is contrasting the input image. This procedure makes it possible to improve the quality of perception of a medical image, to make a preliminary stage of its processing. On the example of images that contain foci of fatty liver lesions, the procedure for contrasting the input image is considered. Examples for real medical images are given, the possibility and expediency of using the contrasting procedure are discussed.
Files
29-39 Boboyorov Sardor Uchqun o‘g‘li.pdf
Files
(931.1 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:96a3d0dfbd5e6b52c96de3d53e5aef90
|
931.1 kB | Preview Download |
Additional details
References
- 1. Castellano, G., Bonilha, L., Li, L. M., & Cendes, F. (2004). Texture analysis of medical images. Clinical radiology, 59(12), 1061-1069.
- 2. He, K., & et al.. (2023). Transformers in medical image analysis. Intelligent Medicine, 3(1), 59-78.
- 3. Stefan, N., & Cusi, K. (2022). A global view of the interplay between non-alcoholic fatty liver disease and diabetes. The lancet Diabetes & endocrinology, 10(4), 284-296.
- 4. Mousavi, S. M. H., Victorovich, L. V., Ilanloo, A., & Mirinezhad, S. Y. (2022, November). Fatty Liver Level Recognition Using Particle Swarm optimization (PSO) Image Segmentation and Analysis. In 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 237-245). IEEE.
- 5. Duan, Y., & et al.. (2022). Association of inflammatory cytokines with non-alcoholic fatty liver disease. Frontiers in immunology, 13, 880298.
- 6. Saiman, Y., Duarte-Rojo, A., & Rinella, M. E. (2022). Fatty liver disease: diagnosis and stratification. Annual review of medicine, 73, 529-544.
- 7. Alharthi, J., & et al.. (2022). Metabolic dysfunction-associated fatty liver disease: a year in review. Current Opinion in Gastroenterology, 38(3), 251-260.
- 8. Ahmad, M. A., & et al.. (2019). Computational complexity of the accessory function setting mechanism in fuzzy intellectual systems. International Journal of Advanced Trends in Computer Science and Engineering, 8(5), 2370-2377.
- 9. Lyashenko, V. V., Babker, A. M. A. A., & Kobylin, O. A. (2016). The methodology of wavelet analysis as a tool for cytology preparations image processing. Cukurova Medical Journal, 41(3), 453-463.
- 10. Lyubchenko, V., Matarneh, R., Kobylin, O., & Lyashenko, V. (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.
- Lyashenko, V., Matarneh, R., & Kobylin, O. (2016). Contrast modification as a tool to study the structure of blood components. Journal of Environmental Science, Computer Science and Engineering & Technology, 5(3), 150-160.