- PII
- 10.31857/S2686954322600562-1
- DOI
- 10.31857/S2686954322600562
- Publication type
- Status
- Published
- Authors
- Volume/ Edition
- Volume 509 / Issue number 1
- Pages
- 94-100
- Abstract
- Novel filtering method in medical images (MRI and US) that are contaminated by noise consisting of mixture speckle and additive noise is designed in this paper. Proposed method consists of several stages: segmentation of image areas, grouping of similar 2D structures in accordance mutual information (MI) measure, homomorphic transformation, 3D filtering approach based on sparse representation in contourlet (CLT) space with posterior filtering in accordance with MI weights similar 2D structures, and final inverse homomorphic transformation. During numerous experiments, the developed method has confirmed their superiority in term of visual image quality via human visual perception as well as in better criteria values, such as PSNR, SSIM, EPI and alfa for different test MRI and US mages corrupted by speckle noise.
- Keywords
- ультразвуковые и магнитно-резонансные изображения суперпикельные методы сегментации фильтрация спекл шум группирование объектов голоморфное преобразование пиковое отношение сигнал/шум
- Date of publication
- 17.09.2025
- Year of publication
- 2025
- Number of purchasers
- 0
- Views
- 12
References
- 1. Кравченко В.Ф., Пономарев В.И., Пустовойт В.И., Аранда-Бохоргес Г. // Доклады РАН. Математика, информатика, процессы управления. 2021. Т. 499. № 2. С. 67–72.
- 2. Aranda-Bojorges G., Ponomaryov V., Reyes-Reyes R., Cruz-Ramos C., Sadovnychiy S. // IEEE Geosci. Rem. Sens. Lett. 2020. V. 19, art. 4018005. https://doi.org/10.1109/LGRS.2021.3108774
- 3. Reyes-Reyes R., Aranda-Bojorges G., Garcia-Salgado B., Ponomaryov V., Cruz-Ramos C., Sadovnychiy S. // Sensors. 2022. V. 22. 5113. https://doi.org/10.3390/s22145113
- 4. Kravchenko V., Perez H., Ponomaryov V. Adaptive Signal Processing of Multidimensional Signals with Applications. Moscow: Fizmatlit, 2009.
- 5. Dabov K., Foi A., Katkovnik V., Egiazarian K. // IEEE Trans. Image Process. 2007. V. 16. № 8. P. 2080–2095.
- 6. Santos C.A.N., Martins D.L.N., Mascarenhas N.D.A. // IEEE Trans. Image Process. 2017. V. 26. 2632–2643. https://doi.org/10.1109/TIP.2017.2685339
- 7. Sameera V.M.S., Sudhish N.G. // Sensing Imaging. 2017. V. 18. P. 1–28. https://doi.org/10.1007/s11220-017-0181-8
- 8. Jubairahmed L., Satheeskumaran S., Venkatesan C. // Clust. Comput. 2019. V. 22. P. 11237–11246.
- 9. Jaburalla M.Y., Lee H.N. // Appl. Sci. 2018. V. 8. 903. P. 1–17. https://doi.org/10.3390/app8060903
- 10. Achanta R., Shaji A., Smith K., Lucchi A., Fua P., Süsstrunk S. // IEEE Trans. Pattern Anal. Mach. Intell. 2012. V. 34. P. 2274–2282.
- 11. Jensen J.A. // Med. Biol. Eng. Comput. 1996. V. 34. P. 351–352.
- 12. Wang Z., Bovik A. // IEEE Signal Process. Mag. 2009. V. 26. № 1. P. 98–117.
- 13. https://openfmri.org/dataset/ (accessed: June21, 2022).
- 14. http://splab.cz/en/download/databaze/ultrasound (accessed: June 19, 2022).
