Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images | Journal of Ambient Intelligence and Humanized Computing Skip to main content
Springer Nature Link
Log in

Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

A clinical ultrasound imaging plays a significant role in the proper diagnosis of patients because, it is a cost-effective and non-invasive technique in comparison with other methods. The speckle noise contamination caused by ultrasound images during the acquisition process degrades its visual quality, which makes the diagnosis task difficult for physicians. Hence, to improve their visual quality, despeckling filters are commonly used for processing of such images. However, several disadvantages of existing despeckling filters discourage the use of existing despeckling filters to reduce the effect of speckle noise. In this paper, two dimensional cuckoo search optimization algorithm based despeckling filter is proposed for avoiding limitations of various existing despeckling filters. Proposed despeckling filter is developed by combining fast non-local means filter and 2D finite impulse response (FIR) filter with cuckoo search optimization algorithm. In the proposed despeckling filter, the coefficients of 2D FIR filter are optimized by using the cuckoo search optimization algorithm. The quantitative results comparison between the proposed despeckling filter and other existing despeckling filters are analyzed by evaluating PSNR, MSE, MAE, and SSIM values for different real ultrasound images. Results reveal that the visual quality obtained by the proposed despeckling filter is better than other existing despeckling filters. The numerical results also reveal that the proposed despeckling filter is highly effective for despeckling the clinical ultrasound images.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Achim A, Bezerianos A, Tsakalides P (2001) Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans Med Imaging 20(8):773–783

    Article  Google Scholar 

  • Atto AM, Pastor D, Mercier G (2009) Smooth adaptation by sigmoid shrinkage. EURASIP J Image Video Process 2009:1–16

    Article  Google Scholar 

  • Boudjelaba K, Chikouche D, Ros F (2011) Evolutionary techniques for synthesis of 2-D FIR filters. In: IEEE statistical signal processing workshop (SSP), pp 601–604

  • Buades A, Coll B, Morel JM (2005) A review of image denoising algorithms with a new one. Multiscale Model Simul 4(2):490–530

    Article  MathSciNet  Google Scholar 

  • Coupe P, Yger P, Prima S, Hellier P, Kerveann C, Barillot C (2008) An optimized blockwise non local means denoising filter for 3D magnetic resonance images. IEEE Trans Med Imaging 27(4):425–441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Coupe P, Hellier P, Kervrann C, Barillot C (2009) Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans Image Process 18(10):2221–2229

    Article  ADS  MathSciNet  PubMed  PubMed Central  Google Scholar 

  • Cronan JJ (2006) Ultrasound is there a future in diagnostic imaging. J Am Coll Radiol 3(9):645–646

    Article  PubMed  Google Scholar 

  • De Paiva JL, Toledo CF, Pedrini H (2016) An approach based on hybrid genetic algorithm applied to image denoising problem. Appl Soft Comput 46:778–791

    Article  Google Scholar 

  • Dhawan AP (2003) Medical image analysts, Wiley, Hoboken

    Google Scholar 

  • Frost VS, Stiles JA, Shanmugan KS, Holtzman J (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell 4(2):157–166

    Article  CAS  PubMed  Google Scholar 

  • Hacini M, Hachouf F, Djemal K (2014) A new speckle filtering method for ultrasound images based on a weighted multiplicative total variation. J Signal Process 103:214–229

    Article  Google Scholar 

  • Hao X, Gao S, Gao X (1994) A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing. IEEE Trans Med Imaging 18:787–794

    Google Scholar 

  • Hillery D, Chin RT (1991) Iterative Wiener filters for images restorations. IEEE Trans Signal Process 39:1901–1904

    Article  ADS  Google Scholar 

  • Kang J, Lee JY, Yoo Y (2016) A new feature-enhanced speckle reduction method based on multiscale analysis for ultrasound B-mode imaging. IEEE Trans Biomed Eng 63(6):1178–1189

    Article  PubMed  Google Scholar 

  • Karaboga N, Getinkaya B (2006) Design of digital FIR filters using differential evolution algorithm. Circuits Syst Signal Process 25(5):649–660

    Article  MathSciNet  Google Scholar 

  • Kaun D, Sawchuck A, Strand T, Chaved P (1985) Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans Pattern Anal Mach Intell 7(2):165–177

    Article  Google Scholar 

  • Kevrann C, Boulanger J (2006) Optimal spatial adaptation for patch based image denoising. IEEE Trans Image Process 15(10):2866–2878

    Article  ADS  Google Scholar 

  • Kockanata S, Karaboga N (2015) A novel 2D-ABC adaptive filter algorithm: a comparative study. J Digit Signal Process 40:140–153

    Article  MathSciNet  Google Scholar 

  • Krissian K, Westin CF, Kikinis R, Vosburgh KG (2007) Oriented speckle reducing anisotropic diffusion. IEEE Trans Image Process 16(5):1412–1424

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  • Latifoglu F (2013) A novel approach to speckle noise filtering based on artificial bee colony algorithm: ultrasound image applications. Comput Methods Progress Biomed 111(3):561–569

    Article  MathSciNet  Google Scholar 

  • Lee JS (1980) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell 2:165–168

    Article  ADS  CAS  PubMed  Google Scholar 

  • Li ZW, Ding XL, Zheng DW, Hung C (2008) Least squares-based filter for remote sensing image noise reduction. IEEE Trans Geosci Remote Sens 46(7):2044–2049

    Article  ADS  Google Scholar 

  • Loupas T, Mc Dicken W, Allan P (1989) An adaptive weighted median filter for speckle suppression in medical ultrasound images. IEEE Trans Circuits Syst 36(1):129–135

    Article  Google Scholar 

  • Luong HQ, Ledda A, Philips W (2006) Non-local image interpolation. In: IEEE international conference on image processing, pp 693–696

  • Malik M, Ahsan F, Mohsin S (2016) Adaptive image denoising using cuckoo algorithm. Soft Comput 20(3):925–938

    Article  Google Scholar 

  • Mastorakis NE, Gonos F (2003) Design of two-dimensional recursive filters using genetic algorithms. IEEE Trans Circuits Syst Fundam Theory Appl 50(5):634–639

    Article  Google Scholar 

  • Ogier A, Hellier P, Barillot C (2006) Restoration of 3D medical images with total variation scheme on wavelet domains (TVW). In: Proceedings of the SPIE medical imaging, vol 6144, pp 465–473

  • Rakhshani H, Rahati A (2016) Snap-drift cuckoo search: a novel cuckoo search optimization algorithm. Appl Soft Comput 52:771–794

    Article  Google Scholar 

  • Ramos-Llorden G, Vegas-Sánchez-Ferrero G, Martin-Fernandez M (2015) Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Trans Image Process 24(1):345–358

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  • Santiago AF, Carlos AL (2006) On the estimation of coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans Image Process 15(9):2694–2701

    Article  Google Scholar 

  • Soni V, Bhandari AK, Kumar A, Singh GK (2013) Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms. IET Signal Proc 7(8):720–730

    Article  Google Scholar 

  • Suresh S, Lal S (2016) An efficient cuckoo search algorithms based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 58(c):184–209

    Article  Google Scholar 

  • Szabo TL (2004) Diagnostic ultrasound imaging: inside out (biomedical engineering). Elsevier Academic Press, Amsterdam

    Google Scholar 

  • Thijssen JM, Obsterveld BJ (1990) Texture in tissue echograms speckle or information? J Ultrasound Med 9(4):215–229

    Article  CAS  PubMed  Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via levy flight. In: IEEE world congress on nature and biologically inspired computing, NaBIC, pp 210–214

  • Yu Y, Acton ST (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11(11):1260–1270

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  • Zhang J, Wu L, Lin G, Cheng Y (2017) An integrated de-speckling approach for medical ultrasound images based on wavelet and trilateral filter. Circuits Syst Signal Process 36(1):297–314

    Article  CAS  Google Scholar 

  • Zhou W, Conrad BA, Rahim SH (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Moradabad Institute of Technology, Moradabad, Uttar Pradesh, 244001, India

    Pradeep K. Gupta & Farooq Husain

  2. Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru, Karnataka, 575025, India

    Shyam Lal

  3. Department of Computer Engineering, Faculty of Engineering, Konya Technical University, Konya, Turkey

    Mustafa Servet Kiran

  4. Department of Computer Engineering, Faculty of Engineering, Selcuk University, Konya, Turkey

    Mustafa Servet Kiran

Authors
  1. Pradeep K. Gupta
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shyam Lal
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mustafa Servet Kiran
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Farooq Husain
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Shyam Lal.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, P.K., Lal, S., Kiran, M.S. et al. Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images. J Ambient Intell Human Comput 15, 921–942 (2024). https://doi.org/10.1007/s12652-018-0891-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-018-0891-3

Keywords