Abstract
A fuzzy logic filter to remove mixed Gaussian and impulsive noise from CT medical images is presented. The method performs a weighted average operation where a fuzzy rule-based model is defined to compute the coefficients. In addition, a parallel filter based on this method is presented. Implementation of the parallel algorithm on multi-core platform using OpenMP is presented. Efficiency is measured in terms of execution time and in terms of PSNR over medical CT images. Experimental results show that mixed Gaussian-impulsive noise is reduced efficiently and image details are preserved. The multi-core implementation allows reducing mixed Gaussian-impulse noise in real-time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gravel, P., Beaudoin, G., De Guise, J.A.: A method for modeling noise in medical images. IEEE Trans. Med. Imaging 23(10), 1221–1232 (2004)
Lei, T., Sewchand, W.: Statistical approach to X-ray CT imaging and its applications in image analysis. II. A new stochastic model-based image segmentation technique for X-ray CT image. IEEE Trans. Med. Imaging 11(1), 62–69 (1992)
Plataniotis, K.N., Venetsanopoulos, A.N.: Color Image Processing and Applications. Springer, Heidelberg (2013)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, ICCV 1998, pp. 839–846. IEEE Computer Society, Washington (1998)
Li, X.: On modeling interchannel dependency for color image denoising. Int. J. Imaging Syst. Technol. 17(3), 163–173 (2007)
Smolka, B.: Peer group switching filter for impulse noise reduction in color images. Pattern Recogn. Lett. 31(6), 484–495 (2010)
Schulte, S., Witte, V.D., Nachtegael, M., der Weken, D.V., Kerre, E.E.: Fuzzy random impulse noise reduction method. Fuzzy Sets Syst. 158(3), 270–283 (2007)
Melange, T., Nachtegael, M., Kerre, E.E.: Fuzzy random impulse noise removal from color image sequences. IEEE Trans. Image Process. 20(4), 959–970 (2011)
Garnett, R., Huegerich, T., Chui, C., He, W.: A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14(11), 1747–1754 (2005)
Passino, K.M., Yurkovich, S., Reinfrank, M.: Fuzzy Control, vol. 42. Addison-Wesley, Menlo Park (1998)
Acknowledgment
This research was supported by the Spanish Ministry of Science, Innovation and Universities (Grant RTI2018-098156-B-C54) co-financed by FEDER funds.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Arnal, J., Pérez, J.B., Vidal, V. (2020). A Parallel Fuzzy Method to Reduce Mixed Gaussian-Impulsive Noise in CT Medical Images. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_104
Download citation
DOI: https://doi.org/10.1007/978-3-030-32456-8_104
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32455-1
Online ISBN: 978-3-030-32456-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)