A feature selection strategy to optimize retinal vasculature segmentation

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thin...

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Autores:
Escorcia-Gutierrez, Jose
Torrents-Barrena, Jordina
Gamarra, Margarita
Madera, Natasha
Romero-Aroca, Pedro
Valls, Aida
Puig, Domenec
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9061
Acceso en línea:
https://hdl.handle.net/11323/9061
https://repositorio.cuc.edu.co/
Palabra clave:
Diabetic retinopathy
Artificial neural networks
Decision trees
Support vector machines
Feature selection
Retinal vasculature segmentation
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openAccess
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© 1997-2020 TSP (Henderson, USA) unless otherwise stated
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repository_id_str
dc.title.eng.fl_str_mv A feature selection strategy to optimize retinal vasculature segmentation
title A feature selection strategy to optimize retinal vasculature segmentation
spellingShingle A feature selection strategy to optimize retinal vasculature segmentation
Diabetic retinopathy
Artificial neural networks
Decision trees
Support vector machines
Feature selection
Retinal vasculature segmentation
title_short A feature selection strategy to optimize retinal vasculature segmentation
title_full A feature selection strategy to optimize retinal vasculature segmentation
title_fullStr A feature selection strategy to optimize retinal vasculature segmentation
title_full_unstemmed A feature selection strategy to optimize retinal vasculature segmentation
title_sort A feature selection strategy to optimize retinal vasculature segmentation
dc.creator.fl_str_mv Escorcia-Gutierrez, Jose
Torrents-Barrena, Jordina
Gamarra, Margarita
Madera, Natasha
Romero-Aroca, Pedro
Valls, Aida
Puig, Domenec
dc.contributor.author.spa.fl_str_mv Escorcia-Gutierrez, Jose
Torrents-Barrena, Jordina
Gamarra, Margarita
Madera, Natasha
Romero-Aroca, Pedro
Valls, Aida
Puig, Domenec
dc.subject.proposal.eng.fl_str_mv Diabetic retinopathy
Artificial neural networks
Decision trees
Support vector machines
Feature selection
Retinal vasculature segmentation
topic Diabetic retinopathy
Artificial neural networks
Decision trees
Support vector machines
Feature selection
Retinal vasculature segmentation
description Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-03-10T19:02:53Z
dc.date.available.none.fl_str_mv 2022-03-10T19:02:53Z
dc.date.issued.none.fl_str_mv 2022
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.spa.fl_str_mv Escorcia-Gutierrez, J., Torrents-Barrena, J., Gamarra, M., Madera, N., Romero-Aroca, P. et al. (2022). A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation. CMC-Computers, Materials & Continua, 70(2), 2971–2989.
dc.identifier.issn.spa.fl_str_mv 1546-2218
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9061
dc.identifier.doi.spa.fl_str_mv doi:10.32604/cmc.2022.020074
dc.identifier.eissn.spa.fl_str_mv 1546-2226
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Escorcia-Gutierrez, J., Torrents-Barrena, J., Gamarra, M., Madera, N., Romero-Aroca, P. et al. (2022). A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation. CMC-Computers, Materials & Continua, 70(2), 2971–2989.
1546-2218
doi:10.32604/cmc.2022.020074
1546-2226
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9061
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Computers, Materials and Continua
dc.relation.references.spa.fl_str_mv 1. J. M. Baena-Díez, J. Peñafiel, I. Subirana, R. Ramos, R. Elosua et al., “Risk of cause-specific death in individuals with diabetes: A competing risks analysis,” Diabetes Care, vol. 39, no. 11, pp. 1987–1995, 2016.
2. R. F. Mansour, “Evolutionary computing enriched computer-aided diagnosis system for diabetic retinopathy: A survey,” IEEE Reviews in Biomedical Engineering, vol. 10, pp. 334–349, 2017.
3. D. Mauricio, N. Alonso and M. Gratacòs, “Chronic diabetes complications: The need to move beyond classical concepts,” Trends in Endocrinology and Metabolism, vol. 31, no. 4, pp. 287–295, 2020.
4. M. M. Nentwich and M. W. Ulbig, “Diabetic retinopathy-ocular complications of diabetes mellitus,” World Journal of Diabetes, vol. 6, no. 3, pp. 489–499, 2015.
5. S. R. Flaxman, R. Bourne, S. Resnikoff, P. Ackland, T. Braithwaite et al., “Global causes of blindness and distance vision impairment 1990–2020: A systematic review and meta-analysis,” The Lancet Global Health, vol. 5, no. 12, pp. e1221–e1234, 2017.
6. S. S. Rahim, V. Palade, J. Shuttleworth and C. Jayne, “Automatic screening and classification of diabetic retinopathy fundus images,” in 15th Int. Conf. on Engineering Applications of Neural Networks, Sofia, Bulgaria, pp. 113–122, 2014.
7. P. Romero-Aroca, S. de la Riva-Fernandez, A. Valls-Mateu, R. Sagarra-Alamo, A. Moreno-Ribas et al., “Cost of diabetic retinopathy and macular oedema in a population, an eight year follow up,” BMC Ophthalmology, vol. 16, no. 1, pp. 1–7, 2016.
8. A. Osareh, B. Shadgar and R. Markham, “A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 4, pp. 535–545, 2009.
9. R. Geetha Ramani and L. Balasubramanian, “Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis,” Biocybernetics and Biomedical Engineering, vol. 36, no. 1, pp. 102–118, 2016.
10. S. W. Franklin and S. E. Rajan, “Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images,” Biocybernetics and Biomedical Engineering, vol. 34, no. 2, pp. 117–124, 2014.
11. S. Moccia, E. De Momi, S. El Hadji and L. S. Mattos, “Blood vessel segmentation algorithms—review of methods, datasets and evaluation metrics,” Computer Methods and Programs in Biomedicine, vol. 158, no. 6801, pp. 71–91, 2018.
12. A. Imran, J. Li, Y. Pei, J. J. Yang and Q. Wang, “Comparative analysis of vessel segmentation techniques in retinal images,” IEEE Access, vol. 7, pp. 114862–114887, 2019.
13. M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka et al., “Blood vessel segmentation methodologies in retinal images–a survey,” Computer Methods and Programs in Biomedicine, vol. 108, no. 1, pp. 407–433, 2012.
14. D. Marín, A. Aquino, M. E. Gegundez-Arias and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Transactions on Medical Imaging, vol. 30, no. 1, pp. 146–158, 2011.
15. D. Adapa, A. N. Joseph Raj, S. N. Alisetti, Z. Zhuang, G. Kaliyaperumal et al., “A supervised blood vessel segmentation technique for digital Fundus images using Zernike moment based features,” PLOS ONE, vol. 15, no. 3, pp. 1–23, 2020.
16. V. Sathananthavathi and G. Indumathi, “BAT algorithm inspired retinal blood vessel segmentation,” IET Image Processing, vol. 12, no. 11, pp. 2075–2083, 2018.
17. D. Kumar, A. Pramanik, S. S. Kar and S. P. Maity, “Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian,” in 2016 Int. Conf. on Signal Processing and Communications, Bangalore, India, pp. 1–5, 2016.
18. R. F. Mansour, “Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy,” Biomedical Engineering Letters, vol. 8, no. 1, pp. 41–57, 2018.
19. T. A. Soomro, A. J. Afifi, J. Gao, O. Hellwich, L. Zheng et al., “Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation,” Expert Systems with Applications, vol. 134, no. 9, pp. 36–52, 2019.
20. L. Luo, D. Chen and D. Xue, “Retinal blood vessels semantic segmentation method based on modified U-Net,” in 2018 Chinese Control And Decision Conf., Shenyang, China, pp. 1892–1895, 2018.
21. R. F. Mansour, A. El Amraoui, I. Nouaouri, V. G. Diaz, D. Gupta et al., “Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems,” IEEE Access, vol. 9, pp. 45137–45146, 2021.
22. A. H. Asad and A. E. Hassaanien, “Retinal blood vessels segmentation based on bio-inspired algorithm,” in Applications of Intelligent Optimization in Biology and Medicine, 1st ed., vol. 96. Canberra, Australia: Springer International Publishing, pp. 181–215, 2016.
23. N. Theera-Umpon, I. Poonkasem, S. Auephanwiriyakul and D. Patikulsila, “Hard exudate detection in retinal fundus images using supervised learning,” Neural Computing and Applications, vol. 32, pp. 1–18, 2019.
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28. S. Pathan, P. Kumar, R. Pai and S. V. Bhandary, “Automated detection of optic disc contours in fundus images using decision tree classifier,” Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 52–64, 2020.
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32. E. Decencière, X. Zhang, G. Cazuguel, B. Lay, B. Cochener et al., “Feedback on a publicly distributed image database: The Messidor database,” Image Analysis & Stereology, vol. 33, no. 3, pp. 231–234, 2014.
33. S. Deladreue, F. Brouaye, P. Bastard, L. Pcligry and A. U. Modeling, “Application of ANOVA methodology to the uncertainties management in power system planning in an open market environment,” in 2001 IEEE Porto Power Tech Proc. (Cat No.01EX502Porto, Portugal, pp. 7–10, 2001.
34. Y. Q. Zhao, X. H. Wang, X. F. Wang and F. Y. Shih, “Retinal vessels segmentation based on level set and region growing,” Pattern Recognition, vol. 47, no. 7, pp. 2437–2446, 2014.
35. S. Roy, A. Mitra, S. Roy and S. K. Setua, “Blood vessel segmentation of retinal image using Clifford matched filter and Clifford convolution,” Multimedia Tools Applications, vol. 78, no. 24, pp. 34839–34865, 2019.
36. F. Argüello, D. L. Vilariño, D. B. Heras and A. Nieto, “GPU-based segmentation of retinal blood vessels,” Journal of Real-Time Image Processing, vol. 14, no. 4, pp. 773–782, 2018.
37. F. Farokhian, C. Yang, H. Demirel, S. Wu and I. Beheshti, “Automatic parameters selection of gabor filters with the imperialism competitive algorithm with application to retinal vessel segmentation,” Biocybernetics and Biomedical Engineering, vol. 37, no. 1, pp. 246–254, 2017.
38. E. Deepika and S. Maheswari, “Earlier glaucoma detection using blood vessel segmentation and classification,” in 2018 2nd Int. Conf. on Inventive Systems and Control, Coimbatore, India, pp. 484–490, 2018.
39. X. Ren, Y. Zheng, Y. Zhao, C. Luo, H. Wang et al., “Drusen segmentation from retinal images via supervised feature learning,” IEEE Access, vol. 6, pp. 2952–2961, 2018.
40. P. T. Karule and S. Joshi, “Blood vessels segmentation using thresholding approach for fundus image analysis,” in 2017 Int. Conf. on Intelligent Computing and Control, Coimbatore, India, pp. 1–5, 2017.
41. J. Dash and N. Bhoi, “An unsupervised approach for extraction of blood vessels from fundus images,” Journal of Digital Imaging, vol. 31, no. 6, pp. 857–868, 2018.
42. Z. Jiang, J. Yepez, S. An and S. Ko, “Fast, accurate and robust retinal vessel segmentation system,” Biocybernetics and Biomedical Engineering, vol. 37, no. 3, pp. 412–421, 2017.
43. S. Roychowdhury, D. D. Koozekanani and K. K. Parhi, “Iterative vessel segmentation of fundus images,” IEEE Transaction on Biomedical Engineering, vol. 62, no. 7, pp. 1738–1749, 2015.
44. V. R. P. Borges, D. J. dos Santos, B. Popovic and D. F. Cordeiro, “Segmentation of blood vessels in retinal images based on nonlinear filtering,” in 2015 IEEE 28th Int. Symp. on Computer-Based Medical Systems, Sao Carlos, Brazil, pp. 95–96, 2015.
45. E. Imani, M. Javidi and H.-R. Pourreza, “Improvement of retinal blood vessel detection using morphological component analysis,” Computer Methods and Programs in Biomedicine, vol. 118, no. 3, pp. 263–279, 2015.
46. S. Pachade, P. Porwal, M. Kokare, L. Giancardo and F. Meriaudeau, “Retinal vasculature segmentation and measurement framework for color fundus and SLO images,” Biocybernetics and Biomedical Engineering, vol. 40, no. 3, pp. 865–900, 2020.
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spelling Escorcia-Gutierrez, JoseTorrents-Barrena, JordinaGamarra, MargaritaMadera, NatashaRomero-Aroca, PedroValls, AidaPuig, Domenec2022-03-10T19:02:53Z2022-03-10T19:02:53Z2022Escorcia-Gutierrez, J., Torrents-Barrena, J., Gamarra, M., Madera, N., Romero-Aroca, P. et al. (2022). A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation. CMC-Computers, Materials & Continua, 70(2), 2971–2989.1546-2218https://hdl.handle.net/11323/9061doi:10.32604/cmc.2022.0200741546-2226Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.15 páginasapplication/pdfengTech Science PressUnited States© 1997-2020 TSP (Henderson, USA) unless otherwise statedAtribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2A feature selection strategy to optimize retinal vasculature segmentationArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.thucamp.com/cmc/v70n2/44677Computers, Materials and Continua1. J. M. Baena-Díez, J. Peñafiel, I. Subirana, R. Ramos, R. Elosua et al., “Risk of cause-specific death in individuals with diabetes: A competing risks analysis,” Diabetes Care, vol. 39, no. 11, pp. 1987–1995, 2016.2. R. F. Mansour, “Evolutionary computing enriched computer-aided diagnosis system for diabetic retinopathy: A survey,” IEEE Reviews in Biomedical Engineering, vol. 10, pp. 334–349, 2017.3. D. Mauricio, N. Alonso and M. Gratacòs, “Chronic diabetes complications: The need to move beyond classical concepts,” Trends in Endocrinology and Metabolism, vol. 31, no. 4, pp. 287–295, 2020.4. M. M. Nentwich and M. W. Ulbig, “Diabetic retinopathy-ocular complications of diabetes mellitus,” World Journal of Diabetes, vol. 6, no. 3, pp. 489–499, 2015.5. S. R. Flaxman, R. Bourne, S. Resnikoff, P. Ackland, T. Braithwaite et al., “Global causes of blindness and distance vision impairment 1990–2020: A systematic review and meta-analysis,” The Lancet Global Health, vol. 5, no. 12, pp. e1221–e1234, 2017.6. S. S. Rahim, V. Palade, J. Shuttleworth and C. Jayne, “Automatic screening and classification of diabetic retinopathy fundus images,” in 15th Int. Conf. on Engineering Applications of Neural Networks, Sofia, Bulgaria, pp. 113–122, 2014.7. P. Romero-Aroca, S. de la Riva-Fernandez, A. Valls-Mateu, R. Sagarra-Alamo, A. Moreno-Ribas et al., “Cost of diabetic retinopathy and macular oedema in a population, an eight year follow up,” BMC Ophthalmology, vol. 16, no. 1, pp. 1–7, 2016.8. A. Osareh, B. Shadgar and R. Markham, “A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 4, pp. 535–545, 2009.9. R. Geetha Ramani and L. Balasubramanian, “Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis,” Biocybernetics and Biomedical Engineering, vol. 36, no. 1, pp. 102–118, 2016.10. S. W. Franklin and S. E. Rajan, “Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images,” Biocybernetics and Biomedical Engineering, vol. 34, no. 2, pp. 117–124, 2014.11. S. Moccia, E. De Momi, S. El Hadji and L. S. Mattos, “Blood vessel segmentation algorithms—review of methods, datasets and evaluation metrics,” Computer Methods and Programs in Biomedicine, vol. 158, no. 6801, pp. 71–91, 2018.12. A. Imran, J. Li, Y. Pei, J. J. Yang and Q. Wang, “Comparative analysis of vessel segmentation techniques in retinal images,” IEEE Access, vol. 7, pp. 114862–114887, 2019.13. M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka et al., “Blood vessel segmentation methodologies in retinal images–a survey,” Computer Methods and Programs in Biomedicine, vol. 108, no. 1, pp. 407–433, 2012.14. D. Marín, A. Aquino, M. E. Gegundez-Arias and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Transactions on Medical Imaging, vol. 30, no. 1, pp. 146–158, 2011.15. D. Adapa, A. N. Joseph Raj, S. N. Alisetti, Z. Zhuang, G. Kaliyaperumal et al., “A supervised blood vessel segmentation technique for digital Fundus images using Zernike moment based features,” PLOS ONE, vol. 15, no. 3, pp. 1–23, 2020.16. V. Sathananthavathi and G. Indumathi, “BAT algorithm inspired retinal blood vessel segmentation,” IET Image Processing, vol. 12, no. 11, pp. 2075–2083, 2018.17. D. Kumar, A. Pramanik, S. S. Kar and S. P. Maity, “Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian,” in 2016 Int. Conf. on Signal Processing and Communications, Bangalore, India, pp. 1–5, 2016.18. R. F. Mansour, “Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy,” Biomedical Engineering Letters, vol. 8, no. 1, pp. 41–57, 2018.19. T. A. Soomro, A. J. Afifi, J. Gao, O. Hellwich, L. Zheng et al., “Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation,” Expert Systems with Applications, vol. 134, no. 9, pp. 36–52, 2019.20. L. Luo, D. Chen and D. Xue, “Retinal blood vessels semantic segmentation method based on modified U-Net,” in 2018 Chinese Control And Decision Conf., Shenyang, China, pp. 1892–1895, 2018.21. R. F. Mansour, A. El Amraoui, I. Nouaouri, V. G. Diaz, D. Gupta et al., “Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems,” IEEE Access, vol. 9, pp. 45137–45146, 2021.22. A. H. Asad and A. E. 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Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Transaction on Medical Imaging, vol. 35, no. 11, pp. 2369–2380, 2016.151270Diabetic retinopathyArtificial neural networksDecision treesSupport vector machinesFeature selectionRetinal vasculature segmentationPublicationORIGINALA Feature Selection Strategy to Optimize Retinal Vasculature Segmentation.pdfA Feature Selection Strategy to Optimize Retinal Vasculature Segmentation.pdfapplication/pdf7212863https://repositorio.cuc.edu.co/bitstreams/b1bbc551-b201-4df1-befb-12a25e0d4693/download25d5ef8842d9a15a1b951c93c63f3311MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/034ac9e9-7dc0-4107-b99c-c4751d822839/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTA Feature Selection Strategy to Optimize Retinal Vasculature Segmentation.pdf.txtA Feature Selection Strategy to Optimize Retinal Vasculature 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