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A Parallel Fuzzy Algorithm for Real-Time Medical Image Enhancement

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A Parallel Fuzzy Algorithm for Real-Time Medical Image Enhancement

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Arnal, J.; Chillarón-Pérez, M.; Parcero, E.; Súcar, LB.; Vidal-Gimeno, V. (2020). A Parallel Fuzzy Algorithm for Real-Time Medical Image Enhancement. International Journal of Fuzzy Systems. 22(8):2599-2612. https://doi.org/10.1007/s40815-020-00953-3

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/161395

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Title: A Parallel Fuzzy Algorithm for Real-Time Medical Image Enhancement
Author: Arnal, Josep CHILLARÓN-PÉREZ, MÓNICA Parcero, Estíbaliz Súcar, Luis B. Vidal-Gimeno, Vicente-Emilio
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
[EN] Medical images may be corrupted by noise. This noise affects the image quality and can obscure important information required for accurate diagnosis. Effectively apply filtering techniques can facilitate diagnosis or ...[+]
Subjects: Filter design , Medical image processing , Fuzzy logic , Noise reduction
Copyrigths: Reserva de todos los derechos
Source:
International Journal of Fuzzy Systems. (issn: 1562-2479 )
DOI: 10.1007/s40815-020-00953-3
Publisher:
Springer-Verlag
Publisher version: https://doi.org/10.1007/s40815-020-00953-3
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098156-B-C54/ES/TECNICAS PARA LA ACELERACION Y MEJORA DE APLICACIONES MULTIMEDIA Y HPC/
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F035/ES/BIOINGENIERIA DE LAS RADIACIONES IONIZANTES. BIORA/
Thanks:
This research was supported by the Spanish Ministry of Science, Innovation and Universities (Grant RTI2018-098156-B-C54) co-financed by FEDER funds.
Type: Artículo

References

Astola, J., Haavisto, P., Neuvo, Y.: Vector median filters. Proc. IEEE 78(4), 678–689 (1990)

Boncelet, C.: Image noise models, pp. 325–335. Academic Press, London (2000)

Camarena, J.G., Gregori, V., Morillas, S., Sapena, A.: Fast detection and removal of impulsive noise using peer groups and fuzzy metrics. J. Vis. Commun. Image Represent. 19(1), 20–29 (2008) [+]
Astola, J., Haavisto, P., Neuvo, Y.: Vector median filters. Proc. IEEE 78(4), 678–689 (1990)

Boncelet, C.: Image noise models, pp. 325–335. Academic Press, London (2000)

Camarena, J.G., Gregori, V., Morillas, S., Sapena, A.: Fast detection and removal of impulsive noise using peer groups and fuzzy metrics. J. Vis. Commun. Image Represent. 19(1), 20–29 (2008)

Camarena, J.G., Gregori, V., Morillas, S., Sapena, A.: Some improvements for image filtering using peer group techniques. Image Vis. Comput. 28(1), 188–201 (2010)

Camarena, J.G., Gregori, V., Morillas, S., Sapena, A.: Two-step fuzzy logic-based method for impulse noise detection in colour images. Pattern Recognit. Lett. 31(13), 1842–1849 (2010)

Camarena, J.G., Gregori, V., Morillas, S., Sapena, A.: A simple fuzzy method to remove mixed gaussian-impulsive noise from color images. IEEE Trans. Fuzzy Syst. 21(5), 971–978 (2013)

Chen, Y., Li, K., Yang, W., Xiao, G., Xie, X., Li, T.: Performance-aware model for sparse matrix-matrix multiplication on the sunway taihulight supercomputer. IEEE Trans. Parallel Distrib. Syst. 30(4), 923–938 (2018)

CUDA Home Page.: https://developer.nvidia.com/cuda-zone (2018). Accessed 12 Dec 2018

Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. Trans. Image Proc. 16(8), 2080–2095 (2007)

Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.O.: Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space. In: Proceedings of the International Conference on Image Processing, ICIP 2007, September 16–19, 2007, San Antonio, Texas, USA, pp. 313–316. IEEE (2007)

Dagum, L., Menon, R.: Openmp: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)

George, A., Veeramani, P.: On some results in fuzzy metric spaces. Fuzzy Sets Syst. 64(3), 395–399 (1994)

Gregori, V., Romaguera, S.: Characterizing completable fuzzy metric spaces. Fuzzy Sets Syst. 144(3), 411–420 (2004)

Kalra, M.K., Maher, M.M., Blake, M.A., Lucey, B.C., Karau, K., Toth, T.L., Avinash, G., Halpern, E.F., Saini, S.: Detection and characterization of lesions on low-radiation-dose abdominal ct images postprocessed with noise reduction filters. Radiology 232(3), 791–797 (2004)

Kalra, M.K., Wittram, C., Maher, M.M., Sharma, A., Avinash, G.B., Karau, K., Toth, T.L., Halpern, E., Saini, S., Shepard, J.A.: Can noise reduction filters improve low-radiation-dose chest ct images pilot study. Radiology 228(1), 257–264 (2003)

Keeling, S.L.: Total variation based convex filters for medical imaging. Appl. Math. Comput. 139(1), 101–119 (2003)

Kenney, C., Deng, Y., Manjunath, B.S., Hewer, G.: Peer group image enhancement. IEEE Trans. Image Process. 10(2), 326–334 (2001)

Li, K., Liu, C., Li, K., Zomaya, A.Y.: A framework of price bidding configurations for resource usage in cloud computing. IEEE Trans. Parallel Distrib. Syst. 27(8), 2168–2181 (2016)

Li, X.: On modeling interchannel dependency for color image denoising. Int. J. Imaging Syst. Technol. 17(3), 163–173 (2007)

Liu, C., Li, K., Xu, C., Li, K.: Strategy configurations of multiple users competition for cloud service reservation. IEEE Trans. Parallel Distrib. Syst. 27(2), 508–520 (2016)

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)

Morillas, S., Gregori, V., Hervás, A.: Fuzzy peer groups for reducing mixed gaussian-impulse noise from color images. IEEE Trans. Image Process. 18(7), 1452–1466 (2009)

Morillas, S., Gregori, V., Peris-Fajarnés, G.: Isolating impulsive noise pixels in color images by peer group techniques. Comput. Vis. Image Underst. 110(1), 102–116 (2008)

Morillas, S., Gregori, V., Peris-Fajarnés, G., Sapena, A.: Local self-adaptive fuzzy filter for impulsive noise removal in color images. Signal Process. 88(2), 390–398 (2008)

OpenMP ARB.: https://www.openmp.org (2018). Accessed 12 Dec 2018

Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

Plataniotis, K.N., Venetsanopoulos, A.N.: Color image processing and applications. Springer, New York (2000)

Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenom. 60(1), 259–268 (1992)

Schulte, S., Huysmans, B., Pižurica, A., Kerre, E.E., Philips, W.: A new fuzzy-based wavelet shrinkage image denoising technique. In: Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems, ACIVS’06, pp. 12–23. Springer-Verlag, Berlin, Heidelberg (2006)

Schulte, S., Morillas, S., Gregori, V., Kerre, E.E.: A new fuzzy color correlated impulse noise reduction method. IEEE Trans. Image Process. 16(10), 2565–2575 (2007)

Schulte, S., Nachtegael, M., Witte, V.D., der Weken, D.V., Kerre, E.E.: A fuzzy impulse noise detection and reduction method. IEEE Trans. Image Process. 15(5), 1153–1162 (2006)

Schulte, S., Witte, V.D., Nachtegael, M., der Weken, D.V., Kerre, E.E.: Fuzzy two-step filter for impulse noise reduction from color images. IEEE Trans. Image Process. 15(11), 3567–3578 (2006)

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)

Smolka, B.: Peer group switching filter for impulse noise reduction in color images. Pattern Recognit. Lett. 31(6), 484–495 (2010)

Smolka, B., Chydzinski, A.: Fast detection and impulsive noise removal in color images. Real-Time Imaging 11(5–6), 389–402 (2005)

Smolka, B., Kusnik, D.: Robust local similarity filter for the reduction of mixed gaussian and impulsive noise in color digital images. Signal Image Video Process. 9(1), 49–56 (2015)

Suckling, J., et al.: The mammographic image analysis society digital mammogram database. Exerpta Med. Int. Congr. Ser. 1069, 375–378 (1994)

Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, ICCV ’98, pp. 839–846. IEEE Computer Society, Washington, DC, USA (1998)

Toprak, A., Güler, I.: Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter. Digit. Signal Process. 17(4), 711–723 (2007)

Wang, Y., Ren, W., Wang, H.: Anisotropic second and fourth order diffusion models based on convolutional virtual electric field for image denoising. Comput. Math. Appl. 66(10), 1729–1742 (2013)

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

Wong, K.K., Fong, S., Wang, D.: Impact of advanced parallel or cloud computing technologies for image guided diagnosis and therapy. J. Xray. Sci. Technol. 25(2), 187–192 (2017)

Xiao, G., Li, K., Li, K.: Reporting l most favorite objects in uncertain databases with probabilistic reverse top-k queries. In: Data Mining Workshop (ICDMW), 2015 IEEE International Conference on, pp. 1592–1599. IEEE (2015)

Xiao, G., Li, K., Li, K.: Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries. Inf. Sci. 405, 207–226 (2017)

Xiao, G., Li, K., Li, K., Zhou, X.: Efficient top-(k, l) range query processing for uncertain data based on multicore architectures. Distrib. Parallel Datab. 33(3), 381–413 (2015)

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