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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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dc.contributor.author Arnal, Josep es_ES
dc.contributor.author CHILLARÓN-PÉREZ, MÓNICA es_ES
dc.contributor.author Parcero, Estíbaliz es_ES
dc.contributor.author Súcar, Luis B. es_ES
dc.contributor.author Vidal-Gimeno, Vicente-Emilio es_ES
dc.date.accessioned 2021-02-16T04:32:41Z
dc.date.available 2021-02-16T04:32:41Z
dc.date.issued 2020-11 es_ES
dc.identifier.issn 1562-2479 es_ES
dc.identifier.uri http://hdl.handle.net/10251/161395
dc.description.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 reduce radiation exposure. In this paper, we introduce a parallel method designed to reduce mixed Gaussian-impulse noise from digital images. The method uses fuzzy logic and the fuzzy peer group concept. Implementations of the method on multi-core interface using the open multi-processing (OpenMP) and on graphics processing units (GPUs) using CUDA are presented. Efficiency is measured in terms of execution time and in terms of MAE, PSNR and SSIM over medical images from the mini-MIAS database and over computed radiography (CR) images generated at different exposure levels. These images have been contaminated with impulsive and/or Gaussian noise. Experiments show that the proposed method obtains good performance in terms of the above mentioned objective quality measures. After applying multi-core and GPUs optimization strategies, the observed time shows that the new filter allows to remove mixed Gaussian-impulse noise in real-time. es_ES
dc.description.sponsorship This research was supported by the Spanish Ministry of Science, Innovation and Universities (Grant RTI2018-098156-B-C54) co-financed by FEDER funds. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof International Journal of Fuzzy Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Filter design es_ES
dc.subject Medical image processing es_ES
dc.subject Fuzzy logic es_ES
dc.subject Noise reduction es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title A Parallel Fuzzy Algorithm for Real-Time Medical Image Enhancement es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s40815-020-00953-3 es_ES
dc.relation.projectID 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/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F035/ES/BIOINGENIERIA DE LAS RADIACIONES IONIZANTES. BIORA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s40815-020-00953-3 es_ES
dc.description.upvformatpinicio 2599 es_ES
dc.description.upvformatpfin 2612 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 8 es_ES
dc.relation.pasarela S\419804 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.description.references Astola, J., Haavisto, P., Neuvo, Y.: Vector median filters. Proc. IEEE 78(4), 678–689 (1990) es_ES
dc.description.references Boncelet, C.: Image noise models, pp. 325–335. Academic Press, London (2000) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references CUDA Home Page.: https://developer.nvidia.com/cuda-zone (2018). Accessed 12 Dec 2018 es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references Dagum, L., Menon, R.: Openmp: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998) es_ES
dc.description.references George, A., Veeramani, P.: On some results in fuzzy metric spaces. Fuzzy Sets Syst. 64(3), 395–399 (1994) es_ES
dc.description.references Gregori, V., Romaguera, S.: Characterizing completable fuzzy metric spaces. Fuzzy Sets Syst. 144(3), 411–420 (2004) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references Keeling, S.L.: Total variation based convex filters for medical imaging. Appl. Math. Comput. 139(1), 101–119 (2003) es_ES
dc.description.references Kenney, C., Deng, Y., Manjunath, B.S., Hewer, G.: Peer group image enhancement. IEEE Trans. Image Process. 10(2), 326–334 (2001) es_ES
dc.description.references 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) es_ES
dc.description.references Li, X.: On modeling interchannel dependency for color image denoising. Int. J. Imaging Syst. Technol. 17(3), 163–173 (2007) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references OpenMP ARB.: https://www.openmp.org (2018). Accessed 12 Dec 2018 es_ES
dc.description.references Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990) es_ES
dc.description.references Plataniotis, K.N., Venetsanopoulos, A.N.: Color image processing and applications. Springer, New York (2000) es_ES
dc.description.references Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenom. 60(1), 259–268 (1992) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references Smolka, B.: Peer group switching filter for impulse noise reduction in color images. Pattern Recognit. Lett. 31(6), 484–495 (2010) es_ES
dc.description.references Smolka, B., Chydzinski, A.: Fast detection and impulsive noise removal in color images. Real-Time Imaging 11(5–6), 389–402 (2005) es_ES
dc.description.references 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) es_ES
dc.description.references Suckling, J., et al.: The mammographic image analysis society digital mammogram database. Exerpta Med. Int. Congr. Ser. 1069, 375–378 (1994) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.description.references 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) es_ES
dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES


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