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Adaptive Fuzzy Based Nonlinear Filter For De-speckling Ultrasound Images

Saranya. B.S*, Ramya, A**, M. Kowar***

*-** Research Scholar, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India.

*** Professor, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India.

Periodicity:December - February'2018
DOI : https://doi.org/10.26634/jcom.5.4.14144

Abstract

Medical image processing is used for analyzing medical images, quantitatively. There exists many medical image modalities like Magnetic Resonance Images (MRI), Computed Tomography (CT), Ultrasound (US), etc. Among them, Ultrasound is a conventional device still practiced in medical field. The Ultrasonic image obtained from US devices is often degraded by the speckle noise. Speckle noise degrades the original image quality; so it needs an efficient denoising for despecklng it. Hence, the authors have found a non-linear denoising filter to remove the speckle noise effectively. In this paper, adaptive fuzzy based non- linear filter has been applied to various ultrasound images which got corrupted with the speckle noise. Experimental results are achieved by calculating the performance metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Signal-to-Noise Ratio (SNR), that shows the workability of the proposed approach. These results are also compared with other median based denoising filters by analytical proportion.

Keywords

Despeckling , Fuzzy, Ultrasonic Images, Denoising.

How to Cite this Article?

Saranya, B.S., Ramya, A., and Murugan, D. (2018). Adaptive Fuzzy Based Nonlinear Filter for Despeckling Ultrasound Images. i-manager’s Journal on Computer Science, 5(4), 1-6. https://doi.org/10.26634/jcom.5.4.14144

References

[1]. Alshennawy, A. A., & Aly, A. A. (2009). Edge detection in digital images using fuzzy logic technique. World Academy of Science, Engineering and Technology, 51, 178-186.

[2]. Devarajan, G., Aatre, V. K., & Sridhar, C. S. (1990). Analysis of median filter. In ACE'90. Proceedings of [XVI Annual Convention and Exhibition of the IEEE In India] (pp. 274-276). IEEE.

[3]. Haußecker, H., & Tizhoosh, H. R. (1999). Fuzzy Image Processing. Handbook of Computer Vision and Applications, (Vol. 2, pp. 683-727). Academic Press.

[4]. Kumbhakarna, V., Patil, V. R., Kawathekar, S. (2015). Review on Speckle Noise Reduction techniques for medical ultrasound image processing. International Journal of Computer Techniques, 2(1), 24-27.

[5]. Liang, Q., & Mendel, J. M. (2000). Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters. IEEE Transactions on Fuzzy Systems, 8(5), 551-563.

[6]. Palabaş, T., & Gangal, A. (2012, July). Adaptive fuzzy filter combined with median filter for reducing intensive salt and pepper noise in gray level images. In Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on (pp. 1-4). IEEE

[7]. Plataniotis, K. N., Androutsos, D., & Venetsanopoulos, A. N. (1996). Fuzzy adaptive filters for multichannel image processing. Signal Processing, 55(1), 93-106.

[8]. Rani, V. (2013). A brief study of various noise model and filtering techniques. International Journal of Global Research in Computer Science, 4(4), 166-171.

[9]. Russo, F. (1996, September). Nonlinear fuzzy filters: An overview. In European Signal Processing Conference, th 1996. EUSIPCO 1996. 8 (pp. 1-4). IEEE.

[10]. Singh, I., & Neeru, N. (2014). Performance comparison of various image denoising filters under spatial domain. International Journal of Computer Applications, 96(19), 21-30.

[11]. Sinha, G. R., & Agrawal, N. (2015). Fuzzy based Image Enhancement Method. IJCA, Proceedings on National Conference on Knowledge, Innovation in Technology and Engineering - NCKITE 2015 (pp. 13-18).

[12]. Suganya, C., & Umamaheswari, O. (2011, August). Image restoration using noise adaptive fuzzy switching weighted median filter for the removal of impulse noise. In Defense Science Research Conference and Expo (DSR), 2011 (pp. 1-4). IEEE.

[13]. Thirumaran, D. J., & Shylaja, S. (2015). Medical Image processing: An Introduction. International Journal of Science and Research, 4(11), 5-611.

[14]. Ville, D. V. D., Nachtegael, M., Weken, D. V. D., Kerre, E. E., Philips, W., & Lemahieu, I. (2003). Noise reduction by fuzzy image filtering. IEEE Transactions on Fuzzy Systems, 11(4), 429-436.

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