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A Comparative Analysis on Various Noise Reduction Techniques Tested for Medical Images


Affiliations
1 Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
2 Department of Computer Science & Engineering, Adi Shankara Institute of Engineering & Technology, Ernakulam, Kerala, India
     

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Noise removing in the medical image is still a challenging task in the research field especially for the imaging devices like mammogram and ultra-sound. Even though advance scanning technology has been invented, these conventional devices play a vital role in scanning the mammogram breast cancer and fetus images. During the image acquisition itself, these images will get corrupted by the physical interference which appears as a noise in an image and affects its visual quality. In this paper we have applied the various traditional and conventional mean based noise removal techniques for the impulse noise corrupted mammogram breast image and standard benchmark. From this comparative analysis we have found that alpha-trimmed mean filters gives the better result than the other filters in terms of PSNR, SSIM and visual quality.

Keywords

Impulse Noise, Wiener Filter, Noise Reduction, Arithmetic Mean Filter, Medical Image.
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  • C. Kadam, and S. B. Borse, “A comparative study of image denoising techniques for medical images,” International Research Journal of Engineering and Technology, vol. 4, no. 6, pp. 369-372, 2017.
  • A. Velayudham, and R. Kanthavel, “A survey on medical image denoising techniques,” International Journal of Advanced Research in Electronics and Communication Engineering, vol. 2, no. 3, p. 272, 2013.
  • M. K. Jhariya, and N. Sahayam, “A literature survey on image denoising & compression.” International Journal for Innovative Research in Multidisciplinary Field, vol. 2, no. 10, pp. 665-667, Oct. 2016.
  • M. Elad, and M. Aharon, “Performance and analysis for non linear filtering algorithm for under water images,” International Journal of Computer Science and Information Security, vol. 58, no. 6, pp. 27-30, November 2012.
  • S. Khera, and S. Malhotra, “Survey on medical image de noising using various filters and wavelet transform,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, no. 4, pp. 230-234, 2014.
  • B. G. Lim, J. C. Woo, and Y. S. Kim, Y. S., “Non-iterative super-resolution technique combining SVA with modified geometric mean filter,” IEEE Geoscience and Remote Sensing Letters, vol. 7, no. 4, pp. 713-717, 2010.
  • H. Ibrahim, N. S. P. Kong, and T. F. Ng, “Simple adaptive median filter for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Consumer Electronics, vol. 54, no. 4, pp. 1920-1927, 2008.
  • E. Nadernejad, “Improvement of nonlinear diffusion equation using relaxed geometric mean filter for low PSNR images,” Electronics Letters, vol. 49, no. 7, pp. 457-458, 2013.
  • S. Q. Yuan, and Y. H. Tan, “Difference-type noise detector for adaptive median filter,” Electronics Letters, vol. 42, no. 8, pp.454-455, 2006.
  • L. Wang, Y. K. Zou, and H. J. Zhang, “A medical image denoising arithmetic based on wiener filter parallel model of wavelet transform,” In IEEE 2nd International Congress on Image and Signal Processing, pp. 1-4, October 2009.
  • G. Liu, and H. Zhong, “Non-local means filter for polarimetric SAR data despeckling based on discriminative similarity measure,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 2, pp. 514-518, 2014.
  • N. Afsham, A. Rasoulian, M. Najafi, P. Abolmaesumi, and R. Rohling, “Non-local means filter-based speckle tracking,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 62, no. 8, pp. 1501-1515, 2015.
  • S. S. Al-amri, and V. N. Kalyankar, “Removal salt & pepper noise from image by using adaptive median filter,” International Journal of Advanced Research in Computer Science, vol. 2, no. 2, pp. 431-434, 2011.
  • A. Ramya, V. Murugan, and D. Murugan, “Non-linear directive contrast filter for mammogram images to enhance pleomorphic calcification,” International Journal of Computer Applications, vol. 163, no. 7, pp. 52-57, 2017.
  • J. Kaur, M. Kaur, P. Kaur, and M. Kaur, “Comparative analysis of image denoising techniques,” International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 6, pp. 296-298, 2012.
  • A. Ramya, B. S. Saranya, D. Murugan, S. V. Kumar, N. Joseph, “Survey on image de-noising based on two- stage median filtering approach,” International Journal of Advanced Research in Computer Engineering & Technology, vol. 6, no. 9, pp. 1494-1498, September, 2017.
  • M. Pathak, S. Singh, and P. Patiala, “Digital image denoising in medical metacarpal images”.
  • P. Deepa, and M. Suganthi, “Performance evaluation of various denoising filters for medical image,” International Journal of Computer Science and Information Technologies, vol. 5, no. 3, pp. 4205-4209, 2014.
  • R. Oten, and R. J. de Figueiredo, Adaptive alpha-trimmed mean filters under deviations from assumed noise model, IEEE Transactions on Image Processing, vol. 13, no. 5, pp. 627-639, 2004.

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  • A Comparative Analysis on Various Noise Reduction Techniques Tested for Medical Images

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Authors

A. Ramya
Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
D. Murugan
Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
T. I. Manish
Department of Computer Science & Engineering, Adi Shankara Institute of Engineering & Technology, Ernakulam, Kerala, India

Abstract


Noise removing in the medical image is still a challenging task in the research field especially for the imaging devices like mammogram and ultra-sound. Even though advance scanning technology has been invented, these conventional devices play a vital role in scanning the mammogram breast cancer and fetus images. During the image acquisition itself, these images will get corrupted by the physical interference which appears as a noise in an image and affects its visual quality. In this paper we have applied the various traditional and conventional mean based noise removal techniques for the impulse noise corrupted mammogram breast image and standard benchmark. From this comparative analysis we have found that alpha-trimmed mean filters gives the better result than the other filters in terms of PSNR, SSIM and visual quality.

Keywords


Impulse Noise, Wiener Filter, Noise Reduction, Arithmetic Mean Filter, Medical Image.

References