Advertisement

Robust Denoising Technique for Ultrasound Images Based on Weighted Nuclear Norm Minimization

  • Shaik Mahaboob BashaEmail author
  • B. C. Jinaga
Conference paper
  • 40 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)

Abstract

Image denoising is an efficacious pre-processing requisite procedure of ultrasound image investigation. In this study two denoising techniques adopted and evaluated to compare their performance. The widespread use of ultrasound images facilitates the diagnosis of various diseases. They pose several challenges and hence efficient pre-processing pipelines are essential to extract useful diagnostic information from the images. Much light is thrown on the Common carotid artery (CCA) images in this study. Two approaches are endorsed for image denoising involving and converting to grayscale for effective diagnosis. Weighted nuclear norm minimization (WNNM) approach is found to be more impressive and better. This also bolstered the validation methods computed in the work. It pretends that the study is useful in extracting diagnostic information. The experimental results impart authenticity to the proposed technique in the adequate analysis of ultrasound images. The principle objective of this work is to aid and accentuate the succeeding processing stages such as segmentation and object recognition to facilitate accurate and exact diagnosis.

Keywords

Image denoising Ultrasound image Weighted nuclear norm minimization (WNNM) Common Carotid Artery (CCA) Structural Symmetry Index Measure (SSIM) and Feature Similarity (FSIM) 

Notes

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    P. Gravel, G. Beaudoin, J.A. De Guise, A method for modeling noise in medical images. IEEE Trans. Med. Imaging 23(10), 1221–1232 (2004)CrossRefGoogle Scholar
  2. 2.
    R. Verma, J. Ali, A comparative study of various types of image noise and efficient noise removal techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(10), 617–622 (2013)Google Scholar
  3. 3.
    M. Zukal, R. Beneš, P. Číka, K. Říha, (n.d.), Towards an optimal interest point detector for measurements in ultrasound images. Meas. Sci. Rev. 13(6), 329–338 (2013)CrossRefGoogle Scholar
  4. 4.
    K. Sumathi et al., Anisotropic diffusion filter based edge enhancement for the segmentation of carotid intima-media layer in ultrasound images using variational level set method without re-initialisation, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE (IEEE, 2014)Google Scholar
  5. 5.
    J.F. Polak et al., Edge-detected common carotid artery intima-media thickness and incident coronary heart disease in the multi-ethnic study of atherosclerosis. J. Am. Heart Assoc., 1–9 (2015)Google Scholar
  6. 6.
    R. Benes, K. Riha, Noise reduction in medical ultrasound images. Electron. Revenue 2(3), 1–8 (2011)Google Scholar
  7. 7.
    M. Xian et al., Automatic breast ultrasound image segmentation: a survey. Pattern Recogn. 79, 340–355 (2018)CrossRefGoogle Scholar
  8. 8.
    S. Mounica, S. Ramakrishnan, B. Thamotharan, A study on preprocessing techniques for ultrasound images of carotid artery, in International Conference on ISMAC in Computational Vision and Bio-Engineering (Springer, Cham, 2018)Google Scholar
  9. 9.
    P.S. Parvaze, S. Ramakrishnan, Extraction of multiple cellular objects in HEp-2 images using LS segmentation. IEIE Trans. Smart Process. Comput. 6(6), 401–408 (2017)CrossRefGoogle Scholar
  10. 10.
    S. Gu et al., Weighted nuclear norm minimization and its applications to low level vision. Int. J. Comput. Vis. 121(2), 183–208 (2017)CrossRefGoogle Scholar
  11. 11.
    S. Gu, L. Zhang, W. Zuo, X. Feng, Weighted nuclear norm minimization with application to image denoising, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014), pp. 2862–2869Google Scholar
  12. 12.
    L. Zhang, L. Zhang, X. Mou, D. Zhang, FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)Google Scholar
  13. 13.
    A. Hore, D. Ziou, Image quality metrics: PSNR vs. SSIM, in 2010 20th International Conference on Pattern Recognition, Istanbul (2010), pp. 2366–2369Google Scholar
  14. 14.
    H. Xu et al., Speckle suppression of ultrasonography using maximum likelihood estimation and weighted nuclear norm minimization, in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2018)Google Scholar
  15. 15.
    A. Buades, B. Coll, J. Morel, A non-local algorithm for image denoising, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, vol. 2 (2005), pp. 60–65Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringGeethanjali Institute of Science and TechnologyNelloreIndia
  2. 2.Department of Electronics and Communication EngineeringJ.N.T. UniversityHyderabadIndia

Personalised recommendations