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Telecommunications and Radio Engineering
SJR: 0.203 SNIP: 0.44 CiteScore™: 1

ISSN Imprimer: 0040-2508
ISSN En ligne: 1943-6009

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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v79.i1.80
pages 81-89

PERCEPTUAL METHOD FOR MRI MEDICAL IMAGES IMPROVEMENT IN PRESENCE OF IMPULSE NOISE

J. K. Kh. Abbas
Al Nisour University College, Nisour seq. 10012, Baghdad, Iraq
A. A-K. Ruhaima
Al Nisour University College, Nisour seq. 10012, Baghdad, Iraq
A. I. Alanssari
Department of Computer Engineering Techniques, Al Nisour University College, Nisour seq. 10012, Baghdad, Iraq
V. V. Pyliavskyi
O.S. Popov Odessa National Academy of Communication, 1 Koval's'ka St, Odessa 65000, Ukraine

RÉSUMÉ

Medical imaging technology gives a good solution in the diagnosis and treatment of patients suffering from a serious illness. Noise removal from medical images is an important task in medical examination to increase the accuracy in disease detection. Diagnostic imaging is a common term for a wide field of medical examinations such as X-ray, ultrasound images, computed tomography and magnetic resonance imaging. Creating clinical human body images by magnetic resonance imaging (MRI) is an important technique in modern medical science. Unfortunately, medical images suffering from various types of noise. This paper focuses on impulse noise detection and removal system in medical scanned images.
The designed system presents a new approach to impulse noise detection in medical images based on visual perceptibility. The detected noise is removed by applying a nonlinear filter to the corrupted pixel only. The proposed method is examined compared with classic filtering procedure and it acts effectively in preserving image details while removing the noise.

RÉFÉRENCES

  1. Brown, Y.-C. R., Cheng, E., Haacke, M., Thompson, R., Venkatesan, (2014) Magnetic Resonance Imaging: Physical Principles and Sequence Design, Wiley.

  2. Geoff Dougherty, (2009) Digital Image Processing for Medical Applications, Cambridge University Press, New York.

  3. Pitas, I. and Venetsanopoulos, A., (1990) Nonlinear Digital Filters, Kluwer.

  4. Domanski, M. and Fettweis, A., (1989) Pseudopassive 2-D recursive digital filters for image processing, Int. Journal on Circuit Theory and Applications, 17, pp.191-195.

  5. Abbas, J. and Domanski, M., (1998) Stable nonlinear filters with spatial prediction, Signal Processing Conference (EUSIPCO 1998), 9th European, pp.1-4.

  6. Abbas, J. and Domanski, M., (2000) A family of efficient nonlinear filters for video restoration, Machine Graphics and Vision 9 (1/2), pp.353-361.

  7. Smith III, J.O., (2007) Introduction to Digital Filters with Audio Applications, BookSurg Publishing, USA.

  8. Sayood, K., (2012) Introduction to Data Compression, Morgan Kaufmann.

  9. Vince, J.A., (2005) Mathematics for Computer Graphics, Springer.

  10. Abbas, J., Alanssari, A., Patlayenko, M., and Pilyavskiy, V., (2019) Improvement to Motion Estimation for High-Efficiency Video Coding, Proceedings of the O.S. Popov ONAT, 1, pp. 112-120.

  11. Eck, D.J., (2018) Introduction to Computer Graphics, Addison-Wesley Professional, Version 1.2.

  12. Bladt, Mogens, Nielsen, and Bo Friis, (2017) Matrix-Exponential Distributions in Applied Probability, Springer.

  13. Ruhaima, A.A., Al-Rudaini, J.K., and Hayder, D.M., (2019) New Design of Noise Prediction in Digital Color Images, International Journal of Research in Computer Applications and Robotics, 7(7), pp 1-6.

  14. Al-Rudaini, J.K., (2013) Prediction Error Processing Technique for Removal and Rejection of Scratches from Video, Al-Ma'mon College Journal, pp.212-221.

  15. Ruhaima, A.A. and Al-Rudaini, J.K., (2019) Impulse Noise Prediction Filter Using Monte-Carlo Simulation, International Journal of Recent Scientific Research, 10(8), pp. 34511-34513.

  16. Alanssari, A.I. and Taher A., (2019) Signal processing in end devices of telecommunication and media paths, Telecommunications and Radio Engineering, 78(17), pp. 1549-1557.


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