Enhancement of Ultrasound Images Using Denoising Filters and Genetic Algorithm

Y. Mahesh*, N. Kiran Kumar**
*-** Department of Electronics and Communication Engineering, VEMU Institute of Technology, Chittoor, India.
Periodicity:October - December'2019
DOI : https://doi.org/10.26634/jdp.7.4.15351

Abstract

Ultrasound images used for medical applications are generally found in low contrast and high noise, generally caused by the environment while capturing the image. Compared to other medical images, denoising an ultrasound image is challenging. The Bayesian shrinkage method has been selected for thresholding based on its sub-band dependency property. The spatial domain based de-noising filtering techniques, using soft thresholding method are compared with the proposed method using Genetic Algorithm (GA). A proposed technique includes GA and results are compared with existing spatial domain based denoising filtering techniques. The proposed algorithm provides enhanced visual clarity for diagnosing the medical images. The proposed method based on GA assesses the better performance on the basis of the quantitative metric like Peak Signal-to-Noise Ratio (PSNR) and Fitness value. The overall simulated result shows that proposed technique outperforms the prevailing denoising filtering methods in terms of preservation of the edges and visual quality of the image.

Keywords

Ultrasound Medical Images, Denoising, Thresholding, Genetic Algorithm, PSNR.

How to Cite this Article?

Mahesh, Y., and Kumar, N. K. (2019). Enhancement of Ultrasound Images using Denoising Filters and Genetic Algorithm. i-manager's Journal on Digital Signal Processing, 7(4), 9-14. https://doi.org/10.26634/jdp.7.4.15351

References

[1]. Ajisha, M. A. T., & Harshita, M. M. S. (2017). Optimal sensor placement and damage detection in structural health monitoring using combined optimization techniques. International Journal of Innovative Works in Engineering and Technology, 3(1), 47-55.
[2]. Goldberg, D. E., & Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Foundations of genetic algorithms (Vol. 1, pp. 69-93). Elsevier. https://doi.org/10.1016/B978-0-08-050684-5.50 008-2
[3]. Gupta, S., Chauhan, R. C., & Saxena, S. C. (2005). Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using speckle modelling based on Rayleigh distribution. IEE Proceedings- Vision, Image and Signal Processing, 152(1), 129-135.
[4]. Gupta, S., Kaur, L., Chauhan, R. C., & Saxena, S. C. (2007). A versatile technique for visual enhancement of medical ultrasound images. Digital Signal Processing, 17(3), 542-560. https://doi.org/10.1016/j.dsp.2006.12.001
[5]. Gupta, S., Kaur, L., Chauhan, R.C., & Saxena, S.C. (2003). A wavelet based statistical approach for speckle reduction in medical ultrasound images. IEEE Proceeding of Convergent Technologies for the Asia Pacific Region, 534-537.
[6]. Gulati, N., & Panwar, P. (2013). Genetic algorithms for image segmentation using active contours. Journal of Global Research in Computer Science, 4(1), 34-37.
[7]. Hashemi, S., Kiani, S., Noroozi, N., & Moghaddam, M. E. (2010). An image contrast enhancement method based on genetic algorithm. Pattern Recognition Letters, 31(13), 1816-1824. https://doi.org/10.1016/j.patrec.200 9.12.006
[8]. Kaur, K., Singh, G., & Singla, A. (2012). Research on spatial filters and homomorphic filtering methods. Journal of Global Research in Computer Science, 3(11), 1-4.
[9]. Kaur, L., Gupta, S., & Chauhan, R. C. (2002, December). Image denoising using wavelet thresholding. In ICVGIP (Vol. 2, pp. 16-18).
[10]. Kaur, P., & Kaur, R. (2014a). A novel approach using image enhancement based on genetic algorithm. In Proceeding of International Conference on Advances in Engineering and Technology (pp.584-589).
[11]. Kaur, R., & Kaur, P. (2014b). A novel approach for despeckling of ultrasound images. International Journal of Computer Science and Mobile Computing, 3(6), 618-622.
[12]. Kaur, R., & Kaur, P. (2014c). Speckle noise reduction in ultrasound images. International Journal of Advanced Research in Computer Science and Software Engineering, 4(3), 998-1001.
[13]. Kaur, S., & Kaur, P. (2015). Review and analysis of various image enhancement techniques. International Journal of Computer Applications Technology and Research, 4(5), 414-418.
[14]. Kaur, P., Singh, G., & Kaur, P. (2016, September). Image enhancement of ultrasound images using multifarious denoising filters and GA. In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 2375-2384). IEEE.
[15]. Krishna, K. S. R., Reddy, A. G., Prasad, M. G., Rao, K. C., & Madhavi, M. (2010). Genetic algorithm processor for image noise filtering using evolvable hardware. International Journal of Image Processing, 4(3), 240-250.
[16]. Liu, Y. (2015). Image denoising method based on threshold, wavelet transform and genetic algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(2), 29-40. https:// doi.org/10.14257/ijsip.2015.8.2.04
[17]. Ma, Q. M., Wang, X. Y., & Du, S. P. (2006). Method and application of wavelet shrinkage denoising based on genetic algorithm. Journal of Zhejiang University-Science A, 7(3), 361-367. https://doi.org/10.1631/jzus.2006.A0361
[18]. Nikravesh, M., & Zadeh, L. A. (Eds.). (2004). Soft computing for information processing and analysis (Vol. 164). Springer Science & Business Media.
[19]. Ranota, H., & Kaur, P. (2014a). A novel approach for image enhancement in digital image processing. In Second International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA.
[20]. Ranota, H., & Kaur, P. (2014b). Review and analysis of image enhancement techniques. International Journal of Information & Computation Technology, 4(6), 583-590.
[21]. Singh, I., & Chand, L. (2013). Image denoising techniques: A review. International Journal of Engineering Research & Technology, 2(4), 1131-1135.
[22]. Tan, H. S. (2001). Denoising of noise speckle in radar image. School of Information Technology and Electrical Engineering. The University of Queensland. Retrieved from http://innovexpo.itee.uq.edu.au/2001/projects/s804 294/thesis.pdf 2001.
[23]. Yan-qing, Z., Abraham, K., Yu, Y. Y., & Young, L. T. (Eds.). (2004). Computational web intelligence: Intelligent technology for web applications (Vol. 58). World Scientific. https://doi.org/10.1142/5525
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