Skip to main content
SpringerLink
Log in

Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The speckle noise commonly occurs in ultrasound imaging based applications. Due to the multiplicative nature, speckle noise deteriorates the visual quality of ultrasound images. This affects the performance of radiologists and practitioners for disease diagnosis and/or patient treatment. The current study proposes a bio-inspired multi-gene genetic programming (MGGP) based intelligent estimator to reduce the speckle noise from ultrasound images. The proposed MGGP approach is based on the parallel framework of multiple genes and has effectively utilized the evolutionary learning capabilities to develop an intelligent estimator, by exploiting the useful statistical features extracted from local neighboring pixels. The performance of the proposed novel approach is evaluated on ultrasound images of common carotid artery corrupted with different noise levels. Further, the robust performance was validated on several diverse types of ultrasound images of Breast Cyst, Kidney Cancer, Liver, Liver Cyst, and Fetal Head. The proposed bio-inspired approach showed superior denoising performance over existing approaches. The proposed intelligent estimator is capable of removing speckle noise effectively while preserving the fine lines and edges. During evolution, the MGGP framework automatically selects the useful statistical features and primitive functions from a wider solution space to develop the intelligent estimator. Further, the proposed approach does not require image-dependent optimal threshold values, as conventional speckle denoising approaches required.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Álvarez-Fernández JA, Núñez-Reiz A (2016) Clinical ultrasound in the ICU: changing a medical paradigm. Medicina Intensiva (English Edition) 40(4):246–249. https://doi.org/10.1016/j.medine.2015.10.003

    Article  Google Scholar 

  2. Atlas of Ultrasound Images (2016) http://www.medison.ru/uzi/eng/all/. Accessed 15–08-2016

  3. Bissacco D (2016) Carotid artery stenosis: the curse of best medical therapy. Angiology. https://doi.org/10.1177/0003319716664609

  4. Choi HH, Lee JH, Kim SM, Park SY (2015) Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique. Biomed Mater Eng 26(Suppl 1):S1587–S1597. https://doi.org/10.3233/BME-151458

    Google Scholar 

  5. Crimmins TR (1985) Geometric filter for speckle reduction. Appl Opt 24(10):1438–1443. https://doi.org/10.1364/AO.24.001438

    Article  Google Scholar 

  6. Donoho DL (1995) De-noising by soft-thresholding. Ieee T Inform Theory 41(3):613–627. https://doi.org/10.1109/18.382009

    Article  MathSciNet  MATH  Google Scholar 

  7. Frost VS, Stiles JA, Shanmugan KS, Holtzman JC (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. Ieee T Pattern Anal PAMI-4(2):157–166. https://doi.org/10.1109/TPAMI.1982.4767223

    Article  Google Scholar 

  8. Gepperth A, Karaoguz C (2016) A bio-inspired incremental learning architecture for applied perceptual problems. Cogn Comput:1–11. https://doi.org/10.1007/s12559-016-9389-5

  9. Gholizadeh S (2016) A review of non-destructive testing methods of composite materials. Procedia Structural Integrity 1:50–57. https://doi.org/10.1016/j.prostr.2016.02.008

    Article  Google Scholar 

  10. Gong G, Zhang H, Yao M (2015) Speckle noise reduction algorithm with total variation regularization in optical coherence tomography. Opt Express 23(19):24699–24712. https://doi.org/10.1364/OE.23.024699

    Article  Google Scholar 

  11. Guo Y, Şengür A, Tian J-W A Novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set. Comput Meth Prog Bio 123:43–53. https://doi.org/10.1016/j.cmpb.2015.09.007

  12. Heckmatt JZ, Leeman S, Dubowitz V (1982) Ultrasound imaging in the diagnosis of muscle disease. J Pediatr 101(5):656–660. https://doi.org/10.1016/S0022-3476(82)80286-2

    Article  Google Scholar 

  13. Huber P, Ronchetti E (2009) Robust statistics. Wiley. doi:citeulike-article-id:13495784 doi: https://doi.org/10.1002/9780470434697

  14. Huynh-Thu Q, Ghanbari M (2008) Scope of validity of PSNR in image/video quality assessment. Electron Lett 44(13):800–801. https://doi.org/10.1049/el:20080522

    Article  Google Scholar 

  15. Javed SG, Majid A, Kausar N (2015) Combining Robust Statistical and 1D Laplacian Operators Using Genetic Programming to Detect and Remove Impulse Noise from Images. In: 2015 13th International Conference on Frontiers of Information Technology (FIT), pp 18–23. doi:https://doi.org/10.1109/FIT.2015.15

  16. Javed SG, Majid A, Mirza AM, Khan A (2016) Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming. Multimed Tools Appl 75(10):5887–5916. https://doi.org/10.1007/s11042-015-2554-0

    Article  Google Scholar 

  17. Javed SG, Majid A, Ali S, Kausar N (2016) A bio-inspired parallel-framework based multi-gene genetic programming approach to Denoise biomedical images. Cogn Comput:1–18. https://doi.org/10.1007/s12559-016-9416-6

  18. Jia ZJ, Song YD, Cai WC (2012) Bio-inspired approach for smooth motion control of wheeled mobile robots. Cogn Comput 5(2):252–263. https://doi.org/10.1007/s12559-012-9186-8

    Article  Google Scholar 

  19. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. Bradford. https://books.google.com.pk/books?id=Bhtxo60BV0EC

  20. Kurnaz MN, Dokur Z, Ölmez T An incremental neural network for tissue segmentation in ultrasound images. Comput Meth Prog Bio 85(3):187–195. https://doi.org/10.1016/j.cmpb.2006.10.010

  21. Lee JS (1980) Digital image enhancement and noise filtering by use of local statistics. Ieee T Pattern Anal PAMI-2(2):165–168. https://doi.org/10.1109/TPAMI.1980.4766994

    Article  Google Scholar 

  22. Lopes A, Touzi R, Nezry E (1990) Adaptive speckle filters and scene heterogeneity. Ieee T Geosci Remote 28(6):992–1000. https://doi.org/10.1109/36.62623

    Article  Google Scholar 

  23. Malutan R, Terebes R, Germain C, Borda M, CisLariu M (2015) Speckle noise removal in ultrasound images using sparse code shrinkage. In: E-Health and Bioengineering Conference (EHB), pp 1–4. https://doi.org/10.1109/EHB.2015.7391394

  24. Medeiros FNS, Mascarenhas NDA, Marques RCP, Laprano CM (2002) Edge preserving wavelet speckle filtering. In: Image Analysis and Interpretation, 2002. Proceedings. Fifth IEEE Southwest Symposium on, 2002. pp 281–285. doi:https://doi.org/10.1109/IAI.2002.999933

  25. Patil P, Dasgupta B (2012) Role of diagnostic ultrasound in the assessment of musculoskeletal diseases. Therapeutic Advances in Musculoskeletal Disease 4(5):341–355. https://doi.org/10.1177/1759720X12442112

    Article  Google Scholar 

  26. Qingju T, Junyan L, Yang W, Hui L (2011) Subsurface interfacial defects of metal materials testing using ultrasound infrared lock-in thermography. Procedia Engineering 16:499–505. https://doi.org/10.1016/j.proeng.2011.08.1116

    Article  Google Scholar 

  27. Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Paper presented at the Proceedings of the International MultiConference of Engineers and Computer Scientists 2010 (IMECS 2010), Hong Kong, 17–19 March, 2010

  28. Steffens HD, Crostack HA (1981) Methods based on ultrasound and optics for the non- destructive inspection of thermally sprayed coatings. Thin Solid Films 83(3):325–342. https://doi.org/10.1016/0040-6090(81)90635-0

    Article  Google Scholar 

  29. Tanabe M (ed) (2011) Ultrasound Imaging. InTech. https://books.google.com.pk/books?id=MaInnQEACAAJ

  30. Ultrasound Image Database (2011) Signal Processing Laboratory. http://splab.cz/en/download/databaze/ultrasound

  31. Ungru K, Tenbrinck D, Jiang X, Stypmann J Automatic classification of left ventricular wall segments in small animal ultrasound imaging. Comput Meth Prog Bio 117(1):2–12. https://doi.org/10.1016/j.cmpb.2014.06.015

  32. Yongjian Y, Acton ST (2002) Speckle reducing anisotropic diffusion. Ieee T Image Process 11(11):1260–1270. https://doi.org/10.1109/TIP.2002.804276

    Article  MathSciNet  Google Scholar 

  33. Zhou W, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. Image Processing, IEEE Transactions on 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by Higher Education Commission, Government of Pakistan under Indigenous PhD Fellowship Program-Batch VII, PIN No. 117-3250-EG7-012. The authors are also grateful to Dr. Dominic Searson for providing help regarding GPTIPS Toolbox.

Author information

Authors and Affiliations

  1. Biomedical Informatics Research Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, P.O. Nilore, Pakistan

    Syed Gibran Javed & Abdul Majid

  2. Department of Biomedical Engineering, College of Medical Science, Catholic University of Daegu Hayangro, Gyeongsan, Gyoungsangbuk-do, Republic of Korea

    Yeon Soo Lee

Authors
  1. Syed Gibran Javed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Majid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yeon Soo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdul Majid.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javed, S.G., Majid, A. & Lee, Y.S. Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images. Multimed Tools Appl 77, 15657–15675 (2018). https://doi.org/10.1007/s11042-017-5139-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5139-2

Keywords

Search

Navigation