A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye...

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Autores:
Pérez, Andrés D.
Perdomo, Oscar
Rios, Hernán
Rodríguez, Francisco
González, Fabio A.
Tipo de recurso:
Part of book
Fecha de publicación:
2020
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
eng
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/1487
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/1487
https://doi.org/10.1007/978-3-030-63419-3_19
Palabra clave:
Fundus of the eye - Diagnosis
Fondo del ojo - Diagnóstico
Diagnóstico por imagen
Diagnostic imaging
Image quality enhancement
Synthetic quality degradation
Eye fundus image
Conditional generative adversarial network
Mejora de la calidad de la imagen
Degradación sintética de la calidad
Imagen del fondo del ojo
Red adversarial generativa condicional
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closedAccess
License
http://purl.org/coar/access_right/c_14cb
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repository_id_str
dc.title.eng.fl_str_mv A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
title A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
spellingShingle A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
Fundus of the eye - Diagnosis
Fondo del ojo - Diagnóstico
Diagnóstico por imagen
Diagnostic imaging
Image quality enhancement
Synthetic quality degradation
Eye fundus image
Conditional generative adversarial network
Mejora de la calidad de la imagen
Degradación sintética de la calidad
Imagen del fondo del ojo
Red adversarial generativa condicional
title_short A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
title_full A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
title_fullStr A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
title_full_unstemmed A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
title_sort A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
dc.creator.fl_str_mv Pérez, Andrés D.
Perdomo, Oscar
Rios, Hernán
Rodríguez, Francisco
González, Fabio A.
dc.contributor.author.none.fl_str_mv Pérez, Andrés D.
Perdomo, Oscar
Rios, Hernán
Rodríguez, Francisco
González, Fabio A.
dc.contributor.researchgroup.spa.fl_str_mv GiBiome
dc.subject.armarc.none.fl_str_mv Fundus of the eye - Diagnosis
topic Fundus of the eye - Diagnosis
Fondo del ojo - Diagnóstico
Diagnóstico por imagen
Diagnostic imaging
Image quality enhancement
Synthetic quality degradation
Eye fundus image
Conditional generative adversarial network
Mejora de la calidad de la imagen
Degradación sintética de la calidad
Imagen del fondo del ojo
Red adversarial generativa condicional
dc.subject.armarc.spa.fl_str_mv Fondo del ojo - Diagnóstico
Diagnóstico por imagen
dc.subject.armarc.eng.fl_str_mv Diagnostic imaging
dc.subject.proposal.spa.fl_str_mv Image quality enhancement
Synthetic quality degradation
Eye fundus image
Conditional generative adversarial network
Mejora de la calidad de la imagen
Degradación sintética de la calidad
Imagen del fondo del ojo
Red adversarial generativa condicional
description Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-05-25T21:57:08Z
2021-10-01T17:16:56Z
dc.date.available.none.fl_str_mv 2021-05-25
2021-10-01T17:16:56Z
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
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dc.identifier.doi.none.fl_str_mv 10.1007/978-3-030-63419-3_19
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identifier_str_mv 0302-9743
10.1007/978-3-030-63419-3_19
url https://repositorio.escuelaing.edu.co/handle/001/1487
https://doi.org/10.1007/978-3-030-63419-3_19
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language eng
dc.relation.ispartofseries.none.fl_str_mv volume 12069
dc.relation.citationedition.spa.fl_str_mv Lecture Notes in Computer Science book series (LNCS, volume 12069)
dc.relation.citationendpage.spa.fl_str_mv 194
dc.relation.citationstartpage.spa.fl_str_mv 185
dc.relation.indexed.spa.fl_str_mv N/A
dc.relation.ispartofbook.spa.fl_str_mv Lecture Notes in Computer Science
Ophthalmic Medical Image Analysis
dc.relation.references.eng.fl_str_mv Perdomo, O., González, F.A.: A systematic review of deep learning methods applied to ocular images. Cienc. Ing. Neogranad 30(1) (2016). https://doi.org/10.18359/rcin.4242
Gharaibeh, N., Al-Hazaimeh, O.M., Al-Naami, B., Nahar, K.M.: An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images. IJSISE 11(4), 206–216. (2018). IEL. https://doi.org/10.1504/IJSISE.2018.093825
Sahu, S., Singh, A.K., Ghrera, S.P., Elhoseny, M.: An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Opt. Laser Technol. 110, 87–98 (2019). https://doi.org/10.1016/j.optlastec.2018.06.061 CrossRefGoogle Scholar
Zhou, M., Jin, K., Wang, S., Ye, J., Qian, D.: Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Trans. Biomed. Eng. 65(3), 521–527 (2017). https://doi.org/10.1109/TBME.2017.2700627 CrossRefGoogle Scholar
Singh, B., Jayasree, K.: Implementation of diabetic retinopathy detection system for enhance digital fundus images. IJATIR 7(6), 874–876 (2015) Google Scholar
Bandara, A.M.R.R., Giragama, P.W.G.R.M.P.B.: A retinal image enhancement technique for blood vessel segmentation algorithm. ICIIS 1–5 (2017). https://doi.org/10.1109/ICIINFS.2017.8300426
Coye, T.: A novel retinal blood vessel segmentation algorithm for fundus images. In: MATLAB Central File Exchange, January 2017 (2015) Google Scholar
Raja, S.S., Vasuki, S.: Screening diabetic retinopathy in developing countries using retinal images. Appl. Med. Inform. 36(1), 13–22 (2015) Google Scholar
Wahid, F.F., Sugandhi, K., Raju, G.: Two stage histogram enhancement schemes to improve visual quality of fundus images. In: Singh, M., Gupta, P.K., Tyagi, V., Flusser, J., Ören, T. (eds.) ICACDS 2018. CCIS, vol. 905, pp. 1–11. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-1810-8_1
Yang, R., Xu, M., Wang, Z., Li, T.: Multi-frame quality enhancement for compressed video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, pp. 6664–6673 (2018).
Vu, T., Nguyen, C.V., Pham, T.X., Luu, T.M., Yoo, C.D.: Fast and efficient image quality enhancement via desubpixel convolutional neural networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 243–259. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_16
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017). https://doi.org/10.1109/CVPR.2017.632
Yoo, T.K., Choi, J.Y., Kim, H.K.: CycleGAN-based deep learning technique for artifact reduction in fundus photography. Graefes Arch. Clin. Exp. Ophthalmol. 258(8), 1631–1637 (2020). https://doi.org/10.1007/s00417-020-04709-5
Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems, pp. 474–485, Academic Press (1994)
Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 48–56. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_6
Pérez, A.D., Perdomo, O., González, F.A.: A lightweight deep learning model for mobile eye fundus image quality assessment. In: Proceedings of SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis (SIPAIM) (2020).
Bartling, H., Wanger, P., Martin, L.: Automated quality evaluation of digital fundus photographs. Acta Ophthalmol. 87(6), 643–647 (2009). https://doi.org/10.1111/j.1755-3768.2008.01321.x
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spelling Pérez, Andrés D.7244cff784b33a16134298e56456744e600Perdomo, Oscarff88a6a3395dc44ade411d38bf28c565600Rios, Hernánf0fbb24b7153739bce2a37777758c8a1600Rodríguez, Francisco6bcbed47fc0f204fe6a9bc98228721ea600González, Fabio A.443fd5ab620c3973efa2055ce58761da600GiBiome2021-05-25T21:57:08Z2021-10-01T17:16:56Z2021-05-252021-10-01T17:16:56Z20200302-9743https://repositorio.escuelaing.edu.co/handle/001/148710.1007/978-3-030-63419-3_19https://doi.org/10.1007/978-3-030-63419-3_19Eye fundus image quality represents a significant factor involved in ophthalmic screening. Usually, eye fundus image quality is affected by artefacts, brightness, and contrast hindering ophthalmic diagnosis. This paper presents a conditional generative adversarial network-based method to enhance eye fundus image quality, which is trained using automatically generated synthetic bad-quality/good-quality image pairs. The method was evaluated in a public eye fundus dataset with three classes: good, usable and bad quality according to specialist annotations with 0.64 Kappa. The proposed method enhanced the image quality from usable to good class in 72.33% of images. Likewise, the image quality was improved from the bad category to usable class, and from bad to good class in 56.21% and 29.49% respectively.La calidad de la imagen del fondo del ojo representa un factor importante en el cribado oftálmico. Normalmente, la calidad de la imagen del fondo del ojo se ve afectada por artefactos, brillo y contraste, lo que dificulta el diagnóstico oftalmológico. Este artículo presenta un método basado en una red generativa condicional para mejorar la calidad de la imagen del fondo del ojo, que se entrena utilizando pares de imágenes sintéticas de mala calidad y buena calidad generadas automáticamente. El método fue evaluado en un conjunto de datos de fondo de ojo público con tres clases: buena, utilizable y mala calidad según las anotaciones de los especialistas con 0,64 Kappa. El método propuesto mejoró la calidad de la imagen de la clase utilizable a la buena en el 72,33% de las imágenes. Asimismo, la calidad de la imagen mejoró de la categoría mala a la clase utilizable, y de la mala a la buena en el 56,21% y el 29,49% respectivamente.Pérez A.D., Perdomo O., Rios H., Rodríguez F., González F.A. (2020) A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement. In: Fu H., Garvin M.K., MacGillivray T., Xu Y., Zheng Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science, vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_1910 páginasapplication/pdfengSpringer VerlagAlemaniavolume 12069Lecture Notes in Computer Science book series (LNCS, volume 12069)194185N/ALecture Notes in Computer ScienceOphthalmic Medical Image AnalysisPerdomo, O., González, F.A.: A systematic review of deep learning methods applied to ocular images. Cienc. Ing. Neogranad 30(1) (2016). https://doi.org/10.18359/rcin.4242Gharaibeh, N., Al-Hazaimeh, O.M., Al-Naami, B., Nahar, K.M.: An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images. IJSISE 11(4), 206–216. (2018). IEL. https://doi.org/10.1504/IJSISE.2018.093825Sahu, S., Singh, A.K., Ghrera, S.P., Elhoseny, M.: An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Opt. Laser Technol. 110, 87–98 (2019). https://doi.org/10.1016/j.optlastec.2018.06.061 CrossRefGoogle ScholarZhou, M., Jin, K., Wang, S., Ye, J., Qian, D.: Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Trans. Biomed. Eng. 65(3), 521–527 (2017). https://doi.org/10.1109/TBME.2017.2700627 CrossRefGoogle ScholarSingh, B., Jayasree, K.: Implementation of diabetic retinopathy detection system for enhance digital fundus images. IJATIR 7(6), 874–876 (2015) Google ScholarBandara, A.M.R.R., Giragama, P.W.G.R.M.P.B.: A retinal image enhancement technique for blood vessel segmentation algorithm. ICIIS 1–5 (2017). https://doi.org/10.1109/ICIINFS.2017.8300426Coye, T.: A novel retinal blood vessel segmentation algorithm for fundus images. In: MATLAB Central File Exchange, January 2017 (2015) Google ScholarRaja, S.S., Vasuki, S.: Screening diabetic retinopathy in developing countries using retinal images. Appl. Med. Inform. 36(1), 13–22 (2015) Google ScholarWahid, F.F., Sugandhi, K., Raju, G.: Two stage histogram enhancement schemes to improve visual quality of fundus images. In: Singh, M., Gupta, P.K., Tyagi, V., Flusser, J., Ören, T. (eds.) ICACDS 2018. CCIS, vol. 905, pp. 1–11. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-1810-8_1Yang, R., Xu, M., Wang, Z., Li, T.: Multi-frame quality enhancement for compressed video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, pp. 6664–6673 (2018).Vu, T., Nguyen, C.V., Pham, T.X., Luu, T.M., Yoo, C.D.: Fast and efficient image quality enhancement via desubpixel convolutional neural networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 243–259. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_16Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017). https://doi.org/10.1109/CVPR.2017.632Yoo, T.K., Choi, J.Y., Kim, H.K.: CycleGAN-based deep learning technique for artifact reduction in fundus photography. Graefes Arch. Clin. Exp. Ophthalmol. 258(8), 1631–1637 (2020). https://doi.org/10.1007/s00417-020-04709-5Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems, pp. 474–485, Academic Press (1994)Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 48–56. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_6Pérez, A.D., Perdomo, O., González, F.A.: A lightweight deep learning model for mobile eye fundus image quality assessment. In: Proceedings of SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis (SIPAIM) (2020).Bartling, H., Wanger, P., Martin, L.: Automated quality evaluation of digital fundus photographs. Acta Ophthalmol. 87(6), 643–647 (2009). https://doi.org/10.1111/j.1755-3768.2008.01321.xhttps://link.springer.com/chapter/10.1007%2F978-3-030-63419-3_19A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality EnhancementCapítulo - Parte de Libroinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttps://purl.org/redcol/resource_type/CAP_LIBhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbFundus of the eye - DiagnosisFondo del ojo - DiagnósticoDiagnóstico por imagenDiagnostic imagingImage quality enhancementSynthetic quality degradationEye fundus imageConditional generative adversarial networkMejora de la calidad de la imagenDegradación sintética de la calidadImagen del fondo del ojoRed adversarial generativa condicionalORIGINALA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement.pdfA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement.pdfCapítulo - Parte de Libroapplication/pdf5425818https://repositorio.escuelaing.edu.co/bitstream/001/1487/5/A%20Conditional%20Generative%20Adversarial%20Network-Based%20Method%20for%20Eye%20Fundus%20Image%20Quality%20Enhancement.pdfdde5ba637ef0373e3f32b19ef8615951MD55metadata only accessTHUMBNAILA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement.pngA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement.pngimage/png136569https://repositorio.escuelaing.edu.co/bitstream/001/1487/4/A%20Conditional%20Generative%20Adversarial%20Network-Based%20Method%20for%20Eye%20Fundus%20Image%20Quality%20Enhancement.pngd6f42ffcd9536829a6bfb671a02cc163MD54open accessA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement.pdf.jpgA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement.pdf.jpgGenerated Thumbnailimage/jpeg11781https://repositorio.escuelaing.edu.co/bitstream/001/1487/6/A%20Conditional%20Generative%20Adversarial%20Network-Based%20Method%20for%20Eye%20Fundus%20Image%20Quality%20Enhancement.pdf.jpg6365184f97a991601c203e9dc9fc733aMD56metadata only accessTEXTA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement.pdf.txtA Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement.pdf.txtExtracted texttext/plain4https://repositorio.escuelaing.edu.co/bitstream/001/1487/3/A%20Conditional%20Generative%20Adversarial%20Network-Based%20Method%20for%20Eye%20Fundus%20Image%20Quality%20Enhancement.pdf.txtce17bbb4d4f1cbe9a2413e4ea88bb0b2MD53open accessLICENSElicense.txttext/plain1881https://repositorio.escuelaing.edu.co/bitstream/001/1487/1/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD51open access001/1487oai:repositorio.escuelaing.edu.co:001/14872022-11-25 03:00:10.691metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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