SUPPRESSION OF SPECKLE NOISE IN MEDICAL IMAGES VIA SEGMENTATION-GROUPING OF 3D OBJECTS USING SPARSE CONTOURLET REPRESENTATION

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

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.

About the authors

V. F. Kravchenko

Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences; Bauman Moscow State Technical University

Author for correspondence.
Email: kvf-ok@mail.ru
Russian Federation, Moscow; Russian Federation, Moscow

Yu. V. Guliaev

Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences

Author for correspondence.
Email: gulyaev@cplire.ru
Russian Federation, Moscow

V. I. Ponomaryov

Instituto Politecnico Nacional de Mexico

Author for correspondence.
Email: vponomar@ipn.mx
Mexico, Mexico

G. Aranda Bojorges

Instituto Politecnico Nacional de Mexico

Author for correspondence.
Email: gibran.aranda.bionics@gmail.com
Mexico, Mexico

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).

Supplementary files

Supplementary Files
Action
1. JATS XML
2.

Download (375KB)
3.

Download (2MB)

Copyright (c) 2023 В.Ф. Кравченко, Ю.В. Гуляев, В.И. Пономарев, Г. Аранда-Бохоргес