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Hybrid Wavelet Transformation and Improved Wavelet Shrinkage Algorithm Method for Reduction of Speckle Noise

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Futuristic Trends in Network and Communication Technologies (FTNCT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 958))

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Abstract

Speckle noise weakens the visual quality of the image thereby limiting the accuracy of Computer aided diagnostic techniques for ultrasound image. An improved method for reduction of multiplicative speckle noise based on Wavelet Shrinkage Guided filter has been proposed in this paper. The Daubechies20 wavelet transformation has been used for the decomposition of the ultrasound images and then an improved wavelet shrinkage algorithm has been utilized for filtering the high-frequency component. The improved quantitative results show the effectiveness of the technique.

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References

  1. Goodman, J.W.: Some fundamental properties of speckle. J. Opt. Soc. Am. 66(11), 1145–1149 (1976)

    Article  Google Scholar 

  2. Burckhardt, C.B.: Speckle in ultrasound B-mode scans. IEEE Trans. Sonic Ultrason. SU-25(1), 1–6 (1978)

    Article  Google Scholar 

  3. Lee, J.S.: Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2(2), 165–168 (1980)

    Article  Google Scholar 

  4. Lee, J.S.: Refined Filtering of Image Noise using Local Statistics. Computer Graphics and Image Processing 15, 380–389 (1981)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Wagner, R.F., Smith, S.W., Sandrik, J.M., Lopez, H.: Statistics of speckle in ultrasound B-scans. IEEE Trans. Sonic Ultrason. 30, 156–163 (1983)

    Article  Google Scholar 

  7. Kuan, D.T., Sawchuk, A.A., Strand, T.C., Chavel, P.: Adaptive restoration of images with speckle. IEEE Trans. Acoust., Speech Signal Process. ASSP-35(3), 373–383 (1987)

    Article  Google Scholar 

  8. Ahmad, M.O., Sundararajan, D.: A fast algorithm for two-dimensional median filtering. IEEE Trans. Circuits Syst. CAS-3(11), 1364–1374 (1987)

    Article  Google Scholar 

  9. Busse, L., Crimmins, T.R., Fienup, J.R.: A model based approach to improve the performance of the geometric filtering speckle reduction algorithm. In: Processing IEEE Ultrasonic Symposium, pp. 1353–1356 (1995)

    Google Scholar 

  10. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In IEEE International Conference on Computer Vision, pp. 839–846 (1998)

    Google Scholar 

  11. Loizou, C., Christodoulou, C., Pattichis, C., Istepanian, R. Pantziaris, M., Nicolaides, A.: Speckle reduction in ultrasound images of atherosclerotic carotid plaque. In: Proceeding of IEEE Conference on DSP, pp. 525–528 (2002)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  13. Buades, A., Coll, B., Morel, J.M.: A review of image de-noising algorithms, with a new one. Multi-Scale Model. Simul. 4, 490–530 (2005)

    Article  Google Scholar 

  14. Loizou, C.P., Pattichis, C.S., Christodoulou, C.I., Istepanian, R.S., Pantziaris, M., Nicolaides, A.: Comparative evaluation of de-speckle filtering in ultrasound imaging of the carotid artery. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 52(10), 1653–1669 (2005)

    Article  Google Scholar 

  15. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson Education, India (2008)

    Google Scholar 

  16. Sivakumar, R., Gayathri, M.K., Nedumaran, D.: Speckle filtering of ultrasound B-scan images–a comparative study between spatial and diffusion filters. In: 2010 IEEE Conference on Open System (ICOS 2010), pp. 80−85 (2010)

    Google Scholar 

  17. Vanithamani, R., Umamaheswari, G.: Wavelet based de-speckling of medical ultrasound images with bilateral filter. In: TENCON 2011 IEEE Region 10 Conference. IEEE (2011)

    Google Scholar 

  18. Sarode, M.V., Deshmukh, P.R.: Reduction of speckle noise and image enhancement of images using filtering technique. Int. J. Adv. Technol. 2(1), 30–38 (2011)

    Google Scholar 

  19. Zhang, J., Wang, C., Cheng, Y.: Comparison of de-speckle filters for breast ultrasound images. Circuits Syst. Signal Process. 34, 185–208 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Asra College of Engineering and Technology, Sangrur, Punjab, India

    Mandeep Kaur, Neeraj Julka & Satish Saini

Authors
  1. Mandeep Kaur
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  2. Neeraj Julka
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  3. Satish Saini
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Corresponding author

Correspondence to Mandeep Kaur .

Editor information

Editors and Affiliations

  1. Jaypee University of Information Technology, Solan, India

    Pradeep Kumar Singh

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

  3. Department of Computer Sciences, Purdue University, West Lafayette, IN, USA

    Bharat Bhargava

  4. National Institute of Technology Kurukshetra, Kurukshetra, India

    Jitender Kumar Chhabra

  5. National Institute of Technology Hamirpur, Hamirpur, India

    Narottam Chand Kaushal

  6. Jaypee University of Information Technology, Solan, India

    Yugal Kumar

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Kaur, M., Julka, N., Saini, S. (2019). Hybrid Wavelet Transformation and Improved Wavelet Shrinkage Algorithm Method for Reduction of Speckle Noise. In: Singh, P., Paprzycki, M., Bhargava, B., Chhabra, J., Kaushal, N., Kumar, Y. (eds) Futuristic Trends in Network and Communication Technologies. FTNCT 2018. Communications in Computer and Information Science, vol 958. Springer, Singapore. https://doi.org/10.1007/978-981-13-3804-5_4

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  • DOI: https://doi.org/10.1007/978-981-13-3804-5_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3803-8

  • Online ISBN: 978-981-13-3804-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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