Vol. 20 No. 5 (2017) Cover Image
Vol. 20 No. 5 (2017)

Published: November 30, 2017

Pages: 1192-1197

Articles

Efficient Approach for De-Speckling Medical Ultrasound Images Using Improved Adaptive Shock Filter

Anas F. Ahmed 1
1 Department of Computer Engineering, Al-Iraqia University, Baghdad, Iraq
History
  • Received: August 10, 2017
  • Available Online: November 3, 2017

Abstract

The problem of filtering medical images is regarded one of the most important challenges that researchers are competing to solve it, where the filtered image helps to get the correct diagnosis of the diseases. This paper introduces an effective approach for filtering the medical ultrasound images. The main type of noise which corrupts the ultrasound images is the speckle noise. There are many methods for de-speckling this type of images addressed by the researchers including classical filters such as Weiner, Kuan, and Lee and adaptive filters such as shock filter. The performance of the proposed approach of this paper is compared with these filters using three performance evaluation metrics: "Peak Signal to Noise Ratio (PSNR)", "Mean Square Error (MSE)", and "Universal Image Quality Index (UIQ)". The empirical results illustrate that the proposed approach outperforms better than the others in term of these evaluation criteria. The proposed approach at noise variance=0.5 achieved the following values: (PSNR=32.0847db, MSE= 0.0962, and UIQ= 0.9829).

Keywords

References

  1. Mateo, L., and Caballero, F., (2009), “Finding out general tendencies in speckle noise reduction in ultrasound images”, ELSEVIER, Vol. 36, Issue. 4, pp. 7786–7797.
  2. Sudeep, V., Palanisamy, P., Rajan, J., Baradan, H., Saba L., Gupta, A., and Suri S., (2016), “ Speckle reduction in medical ultrasound images using an unbiased non-local means method”, ELSEVIER, Vol. 28, pp. 1-8.
  3. Bahateja, V., Singh, G., Srivastava, A. and Singh, J., (2014), “Speckle Reduction in Ultrasound Images Using an Improved Conductance Function Based on Anisotropic Diffusion”, IEEE Int. Conf. Computing for Sustainable Global Development, pp. 619-624.
  4. Njeh, I. Sassi, B. Chtourou, K., and Hamada, A., (2011), “Speckle Noise Reduction in Breast Ultrasound Images: SMU Approach”, IEEE Int. Conf. Systems, signals and systems, pp. 1-6.
  5. Kyriacou, C., Pattichis, C., Pattichis, M., Louizou, C., Christodoulou, C., Kakkos, K., and Nicolaides, A., (2010), “A Review of Noninvasive Ultrasound Image Processing Methods in the Analysis of Carotid Plaque Morphology for the Assessment of Stroke Risk”, IEEE Trans. Information Technology in Biomedicine Vol. 14, No. 4, pp. 1027-1038.
  6. Cm, W., Yc, C., and Ks, H., (1992), “Texture features for classification of ultrasonic liver images”, IEEE Trans. Medical Imaging, Vol .11, Issue. 2, pp. 141-152.
  7. Herimath, S., Akkasaligar, T., and Badiger, S., (2013), “Advancements and Breakthroughs in Ultrasound Imaging”, INTECH, pp. 300.
  8. Suhaila, S., and Shimamura, T., (2010), “Power spectrum estimation method for image denoising by frequency domain Wiener filter”, IEEE Int. Conf. Computer and Automation Engineering, Vol. 3, No. 1, pp. 608-612.
  9. Terebes, R., Borda, M., Germain, C., and Lavialle, O., “A Novel Shock Filter for Image Restoration and Enhancement”, (2012), IEEE Int. Conf. Signal Processing, pp. 255-259.
  10. Battiato, S., Gallo, G., and Stanco, F., “ Smart Interpolation by Anisotropic Diffusion”, (2003), IEEE Int. Conf. Image Analysis and Processing, pp. 572-577.
  11. Alvarez, L., and Mazzora, L., (1994), “ Signal and image restoration using shock filters and anisotropic diffusion”, SIAM Journal, Vol. 31, Issue. 2, pp. 590-605.
  12. Wang, Z., and Bovic, C., (2002), “A Universal Image Quality Index”, IEEE Letter. Signal Processing, Vol. 9, Issue. 3, pp. 81-84.