Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial
La retinopatía diabética es una de las afecciones más comunes de la diabetes [1], siendo la tercera causa de ceguera irreversible en el mundo, pero la primera en personas de edad productiva (16 a 64 años) que padecen de diabetes tipo 1 o tipo 2 por más de 10 años, los cuales cuentan con el 50% a 60%...
- Autores:
-
Aza Mantilla, Jessica Paola
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2018
- Institución:
- Universidad Autónoma de Bucaramanga - UNAB
- Repositorio:
- Repositorio UNAB
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unab.edu.co:20.500.12749/1614
- Acceso en línea:
- http://hdl.handle.net/20.500.12749/1614
- Palabra clave:
- Mechatronic Engineering
Computer vision
Artificial intelligence
Medicine
Biomedical engineering
Apparatus and instruments
Investigations
Analysis
Diabetic retinopathy
Automation
Artificial vision
Ingeniería mecatrónica
Visión por computador
Inteligencia artificial
Medicina
Ingeniería biomédica
Aparatos e instrumentos
Investigaciones
Análisis
Retinopatía diabética
Automatización
Visión artificial
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial |
| dc.title.translated.eng.fl_str_mv |
Design of an automatic system to determine the degree of progression of diabetic retinopathy using artificial vision techniques |
| title |
Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial |
| spellingShingle |
Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial Mechatronic Engineering Computer vision Artificial intelligence Medicine Biomedical engineering Apparatus and instruments Investigations Analysis Diabetic retinopathy Automation Artificial vision Ingeniería mecatrónica Visión por computador Inteligencia artificial Medicina Ingeniería biomédica Aparatos e instrumentos Investigaciones Análisis Retinopatía diabética Automatización Visión artificial |
| title_short |
Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial |
| title_full |
Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial |
| title_fullStr |
Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial |
| title_full_unstemmed |
Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial |
| title_sort |
Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial |
| dc.creator.fl_str_mv |
Aza Mantilla, Jessica Paola |
| dc.contributor.advisor.spa.fl_str_mv |
González Acevedo, Hernando |
| dc.contributor.author.spa.fl_str_mv |
Aza Mantilla, Jessica Paola |
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González Acevedo, Hernando [0000544655] |
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González Acevedo, Hernando [V8tga0cAAAAJ&hl=es] |
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González Acevedo, Hernando [55821231500] |
| dc.contributor.researchgate.*.fl_str_mv |
González Acevedo, Hernando [Hernando-Gonzalez] |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Investigación Control y Mecatrónica - GICYM Grupo de Investigaciones Clínicas |
| dc.subject.keywords.eng.fl_str_mv |
Mechatronic Engineering Computer vision Artificial intelligence Medicine Biomedical engineering Apparatus and instruments Investigations Analysis Diabetic retinopathy Automation Artificial vision |
| topic |
Mechatronic Engineering Computer vision Artificial intelligence Medicine Biomedical engineering Apparatus and instruments Investigations Analysis Diabetic retinopathy Automation Artificial vision Ingeniería mecatrónica Visión por computador Inteligencia artificial Medicina Ingeniería biomédica Aparatos e instrumentos Investigaciones Análisis Retinopatía diabética Automatización Visión artificial |
| dc.subject.lemb.spa.fl_str_mv |
Ingeniería mecatrónica Visión por computador Inteligencia artificial Medicina Ingeniería biomédica Aparatos e instrumentos Investigaciones Análisis |
| dc.subject.proposal.spa.fl_str_mv |
Retinopatía diabética Automatización Visión artificial |
| description |
La retinopatía diabética es una de las afecciones más comunes de la diabetes [1], siendo la tercera causa de ceguera irreversible en el mundo, pero la primera en personas de edad productiva (16 a 64 años) que padecen de diabetes tipo 1 o tipo 2 por más de 10 años, los cuales cuentan con el 50% a 60% de probabilidad de desarrollarla. Para identificar la condición médica bajo una observación física del órgano ocular se realiza un examen con lámpara de hendidura o una cámara de fondo de ojo con pupilas dilatadas [2]. Dichas formas de diagnóstico consumen tiempo y con frecuencia requieren una angiografía con fluoresceína o una tomografía de coherencia óptica para confirmar el grado de la retinopatía diabética. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018-08-13 |
| dc.date.accessioned.none.fl_str_mv |
2020-06-26T19:45:26Z |
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2020-06-26T19:45:26Z |
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info:eu-repo/semantics/bachelorThesis |
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Trabajo de Grado |
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http://purl.org/coar/resource_type/c_7a1f |
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http://purl.org/redcol/resource_type/TP |
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Aza Mantilla, Jessica Paola (2018). Diseño de un sistema automático para determinar del grado de progresión de retinopatía diabética utilizando técnicas de visión artificial. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNAB [1] FUNDACIÓN PARA LA DIABETES, CON LA COLABORACIÓN DE IER BAVIERA. Infografía "La retina. Órgano diana de la diabetes"[en línea]. 14 de noviembre, 2014. Disponible en: http://www.fundaciondiabetes.org/general/material/11/infografia-la-retina-organo-diana-de-la-diabetes [2] TYLER, Marshall E. y SAINE, Patrick J. Excerpted from: "Ophthalmic photography: retinal photography, angiography, and electronic imaging, 2nd edition" [en línea]. Butterworth-Heinemann Medical. Disponible en: http://www.opsweb.org/?page=fundusphotography [3] [25] ETIENNE Decencière, Xiwei Zhang, Guy Cazuguel, Bruno Lay, Béatrice Cochener, Caroline Trone, Philippe Gain, Richard Ordonez, Pascale Massin, Ali Erginay, Béatrice Charton, Jean-Claude Klein. Feedback on a publicly distributed database: the Messidor database. Image Analysis & Stereology, v. 33, n. 3, p. 231-234, Agosto. 2014. ISSN 1854-5165. Disponible en: http://www.ias-iss.org/ojs/IAS/article/view/1155 [4] WANG, Huan. HSU, Wynne. GOH, Kheng Guan, LEE, Mong Li. "An Effective Approach to Detect Lesions in Color Retinal Images". Publicado online: National University of Singapore in 2000 [5] HOOVER, Adam. KOUZNETSOVA, Valentina. GOLDBAUM, Michael. "Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter" Response. IEEE transactions on medical imaging, vol. 19, no. 3, march 2000 [6] GRISAN, Enrico. PESCE, Alessandro. GIANI, Alfredo, FORACCHIA, Marco. RUGGERI, Alfredo. "A new tracking system for the robust extraction of retinal vessel structure" Proceedings of the 26th Annual International Conference of the IEEE EMBS. San Francisco, CA, USA • September 1-5, 2004 [7] VALLABHA, Deepika. DORAIRAJ, Ramprasath. NAMUDURI, Kamesh. THOMPSON, Hilary. "Automated Detection and Classification of Vascular Abnormalities in Diabetic Retinopathy". At IEEE 2004 [8] FRANKLIN, Sundararaj W; RAJAN, Samuelnadar E. "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images." Published in IET Image Processing, 2013. ZELJKOVIĆ, Vesna; BOJIC, [9] ZELJKOVIĆ, Vesna; BOJIC, Milena; TAMEZE, Claude; VALEV, Ventzeslav."Exudates detection and classification algorithm of diabetic patients’ retina images", Journal of Circuits, Systems, and Computers Vol. 22, No. 2 (2013) 1250087 (15 pages) [10] SHAHBEIG, Saleh. "Automatic and quick blood vessels extraction algorithm in retinal images." Published in IET Image Processing on 10th January 2013 [11] WANG, Huan. HSU, Wynne. GOH, Kheng Guan, LEE, Mong Li. "An Effective Approach to Detect Lesions in Color Retinal Images." Publicado online: National University of Singapore in 2000 [12] YUN, Wong Li. U. ACHARYA, Rajendra. Venkatesh, Y.V. Chee, Caroline c. MIN, Lim Choo. NG, E.Y.K. d. "Identification of different stages of diabetic retinopathyusing retinal optical images" Publicación en “Actualización de medicina de familia” [En línea] , URL: http://amf-semfyc.com/web/article_ver.php?id=1016 [13] FADZIL, Ahmad; IZHAR, Lila Iznita; NUGROHO, Hermawan; NUGROHO, Hanung Adi. "Analysis of retinal fundus images for grading of diabetic retinopathy severity." Publicado online: 27 de Enero, 2011 en International Federation for Medical and Biological Engineering 2011 [14] TARIQ, Anam; AKRAM, Usman; SHAUKAT, Arslan; KHAN, Shoab A. "Automated Detection and Grading of Diabetic Maculopathy in Digital Retinal Images." Publicado online: 17 de Enero, 2013 en Society for Imaging Informatics in Medicine 2013 [15] OLOUMI, Faraz; RANGAYYAN, Rangaraj M.; ELLS Anna L. Computer-aided "Diagnosis of Proliferative Diabetic Retinopathy via Modeling of the Major Temporal Arcade in Retinal Fundus Images." Publicado online: 16 de Abril, 2013 en Society for Imaging Informatics in Medicine 2013 [16] [17] FUNDACIÓN OFTALMOLÓGICA NACIONAL. "Retinopatía Diabética." Disponible en línea, URL: http://fon.org.co/ [18] REVUELTA, Araceli Fernández. Especialista en Medicina Familiar y Comunitaria Profesora asociada. Universidad de Zaragoza Técnica de exploración del fondo de ojo. Publicación en “Actualización de medicina de familia” [En línea], URL: http://amf-semfyc.com/web/article_ver.php?id=1016 [19] NHS DIABETIC EYE SCREENING PROGRAMME. "Diabetic Eye Screening Feature Based Grading Forms." Version 1.4, 1 November 201 [20] DONAYRE, Dr. Juan Vásquez. Retinopatía Diabética (R.D.) "Oftalmología Médica I". [En línea], URL: http://sisbib.unmsm.edu.pe/bibvirtual/libros/medicina/cirugia/tomo_iv/oftal_med1.ht m [21] [22] RUDAS, Jorge. SÁNCHEZ Torres, Germán "Detección de patologías derivadas de las afecciones diabéticas: una revisión del análisis digital de imágenes de retina" [23] M. ORTEGA, M. G. Penedo, J. Rouco, N. Barreira, M. J. Carreira, "Retinal verification using a feature points based biometric pattern", EURASIP Journal on Advances in Signal Processing, vol. 2009, Article ID 235746, 13 pp., 2009. [24] A. Hoover, V. Kouznetsova and M. Goldbaum, "Locating Blood Vessels in Retinal Images by Piece-wise Threhsold Probing of a Matched Filter Response", IEEE Transactions on Medical Imaging , vol. 19 no. 3, pp. 203-210, March 2000. [26] FAUSETT, Laurene V. “Fundamentals of Neural Networks Architectures, Algorithms, and appliations”. Prentice-Hall (1994). [27] R. Santiago Montero, J. M. López Márquez, M. Ornelas Rodríguez. "Aplicando filtros de Gabor a segmentación de caracteres alfanuméricos en placas vehiculares." X Congreso Internacional sobre Innovación y Desarrollo Tecnológico, 23 al 25 de noviembre de 2011, Cuernavaca Morelos, México [28] KOVESI, Peter. "MATLAB and Octave Functions for Computer Vision and Image Processing. - What Are Log-Gabor Filters and Why Are They Good?" Centre for Exploration Targeting School of Earth and Environment The University of Western Australia. [En línea], URL: http://www.csse.uwa.edu.au/~pk/research/matlabfns [29] CUEVAS, Erik. ZALDIVAR, Daniel. PÉREZ, Marco. "Procesamiento digital de imágenes con Matlab y Simulink." 2010 Alfaomega Gurpo Editor S.A de C. [30] DUDA, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp. 11–15 (Enero, 1975 [31] SUBBAN, R; KALIAPERUMAL, P; JAYASHREE, M; CAHNDRAN, R. “Eye retinal blood vessel segmentation methods” International Journal of Future Innovative Science and Technology, Mayo de 2015. [32] HALOI, Mrinal. “Improved micro-aneurysm detection using neural networks”. Indian Institute of Technology, Guwahati, Mayo de 2015 [33] GAIKWARD, N; BADADAPURE, P. “Image processing technique for hard exudates detection for diagnosis of diabetic retinopathy” International Journal on recent and innovation trends in computing and communication, Abril de 2015. [34] CYBERNETICS GROUP, “Diabetic retinopathy image enhancement using CLAHE by programming TMS320C6416”. RV Colege Egineering. [35] YU, Ting; MA, Yide; LI, Weng. “Automatic detection of exudates and fovea for grading of diabetic macular edema in fundus image”. Lanzhou University, China. [36] AL-RAWI M., QUTAISHAT M., ARRAR M., "An improved matched filter for blood vessel detection of digital retinal images", Computers in Biology & Medicine, vol. 37, no. 2, pp. 262-267, 2007. [37] DOUGHERTY G., JOHSON M. J., WIERS M., "Measurement of retinal vascular tortuosity and its application to retinal pathologies", Journal of Medical & Biological Engineering & Computing, vol. 48, no. 1, pp. 87-95, 2010. [38] CHAUDHURI S., CHATTERJEE S., KATZ N., NELSON M., and GOLDBAUM M. "Detection of blood vessels in retinal images using two 114 dimensional matched filters", IEEE transactions on Medical Imaging, vol. 8, no. 3, pp. 263–269. 1989a [39] PATTON N., TARIQ M. A., MACGILLIVRAYD T., DEARYE I. J., BALJEAN D., ROBERT H. E., KANAGASINGAM Y., CONSTABLE J., "Retinal image analysis: Concepts, applications and potential", Progress in Retinal and Eye Research, vol. 25, no. 1, pp. 99-127, 2005 [40] CHANG C. I., DU Y., WANG J., GUO S. M., THOUIN P. D., "Survey and comparative analysis of entropy and relative entropy thresholding techniques", IEE Proceedings of Vision, Image and Signal Processing, vol. 153, no. 6, pp. 837-850, 2006. [41] WU D., ZHANG M., LIU J. C., and BAUMAN W., "On the adaptive detection of blood vessels in retinal images", IEEE Transactions on Biomedical Engineering, vol. 53, no. 2, pp. 341-343, 2006. [42] AKITA K., KUGA H., "A computer method of understanding ocular fundus images", Pattern Recognition, vol. 15, no. 6, pp. 431–443, 1982. [43] SINTHANAYOTHIN C., KONGBUNKIAT V., PHOOJARUENCHANACHAI S., SINGALAVANIJA A., "Automated Screening System for Diabetic Retinopathy", Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis , pp. 915-920, 2003. [44] SINTHANAYOTHIN C., BOYCE J. F., WILLIAMSON T. H., COOK H. L., MENSAH E., LAL S., and USHER D., "Automated detection of diabetic retinopathy on digital fundus images", Diabetic medicine, vol. 19, no. 2, pp. 105-112, 2002. [45] WARD N., Tomlinson S., and Taylor C., "Image analysis of fundus photographs", Ophthalmology, vol. 96, no. 1, pp. 80-86, 1989 [46] LIU Z. Q., CAI J., and BUSE R., "Hand-writing Recognition: Soft Computing and Probablistic Approaches", Springer Verlag, Berlin, 2003 [47] WANG H., HSU W., GOH K. G., and LEE M., “An effective approach to detect lesions in colour retinal images“, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 181-187, 2000. [48] GARDNER G., Keating D., Williamson T. H., and Elliot A. T., “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool", British Journal of Ophthalmology, vol. 80, no. 11, pp. 940–944, 1996. [49] OSAREH A., Mirmehdi M., Thomas B., and Markham R., “Colour morphology and snakes for optic disc localization", Proceedings of 6th Conference on Medical Image Understanding and Analysis, pp. 21-24, 2002. [50] KANSKI, J J., Bowling, B., “Oftalmología Clínica“, Edición en español de la séptima edición de la obra original en inglés “Clinical Opthalmology. A systematic approach“. Elsevier, España.2012 [51] LI Huiqi., and Opas C., “Automatic location of optic disc in retinal images‖, Proceedings of the International Conference on Image Processing", vol. 2, pp. 837-840, 2001. [52] LI Huiqi., and Opas C., “Automated Feature Extraction in Color Retinal Images by a Model Based Approach", IEEE Transactions Biomedical Engineering, vol. 51, no. 2, pp. 246-254, 2004 [53] C. MARINO, E. Ares , M.G.Penedo, M. Ortega, N. Barreira, F. Gomez-Ulla, “Automated Three Stage Red Lesions Detection In Digital Color Fundus Images”, WSEAS Transactions on Computers, vol. 7, 2008, pp. 207-215. [54] SAI Prasad, Arpit Jain and Anurag Mittal, “Automated Feature Extraction for Early Detection of Diabetic Retinopathy in Fundus Images”, Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp.210-217. [55] BALASUBRAMANIAN, S., Pradhan Sandip and Chandrasekaran, V. “Red Lesion Detection in Digital Fundus Images“, Proc. International Conference on Image Processing, 2008, pp. 2932-2935. [56] GONZALES R. C., and Woods R. E., “Digital image processing”, 2nd Edition, Pearson Education, pp. 94-101, 2004. [57] USHER D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening”, Diabet Med 2004;21:84–90. [58] ZHENG Liu, Opas C, Krishnan SM. Automatic image analysis of fundus photograph. In: Proceedings of the International Conference on Engineering in Medicine and Biology, vol. 2. 1997. p. 524–5. [59] VARPA Group, VARIA DATABASE. Disponible en línea, URL: http://www.varpa.es/research/biometrics.htm. [60] GOLDBAUM, Michael M.D. U.S. National Institutes of Health. STARE DATABASE. Disponible en línea, URL: http://cecas.clemson.edu/~ahoover/stare/. [61] MSC MEDICAL IMAGING, UTRECHT UNIVERSITY. DRIVE DATABASE. Disponible en línea, URL: https://www.isi.uu.nl/Research/Databases/DRIVE/download.php. [62] KAUPPI, Tomi et al. DIARETDB DATABASE. Disponible en línea, URL: http://www2.it.lut.fi/project/imageret/diaretdb1_v2_1/ [63] VEERANNA, Preethi. A Study On Diabetic Retinopathy Using Digital Image Processing Techniques. Gulbarga University. Disponible en línea, URL: http://hdl.handle.net/10603/137847 [64] DECENCIÈRE E, et al. TeleOphta: Machine learning and image processing methods for teleophthalmology.IRBM (2013), Disponible en línea, URL: http://dx.doi.org/10.1016/j.irbm.2013.01.01 [65] H. LI and O. Chutatape, “A model based approach for automated feature extraction in fundus images,” Proc. 9th IEEE International Conference on Computer Vision, Nice, France, October 2003. [66] ADARSH. P and D. Jeyakumari. Multiclass SVM-Based Automated Diagnosis of Diabetic Retinopathy. International conference on Communication and Signal Processing, April 3-5, 2013, Ind |
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González Acevedo, Hernando490b15a6-3d80-4525-a9a0-44e34b8f0937-1Aza Mantilla, Jessica Paola7d1ce56a-2de6-424f-a5f3-751742e5ede6-1González Acevedo, Hernando [0000544655]González Acevedo, Hernando [V8tga0cAAAAJ&hl=es]González Acevedo, Hernando [55821231500]González Acevedo, Hernando [Hernando-Gonzalez]Grupo de Investigación Control y Mecatrónica - GICYMGrupo de Investigaciones Clínicas2020-06-26T19:45:26Z2020-06-26T19:45:26Z2018-08-13http://hdl.handle.net/20.500.12749/1614instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABLa retinopatía diabética es una de las afecciones más comunes de la diabetes [1], siendo la tercera causa de ceguera irreversible en el mundo, pero la primera en personas de edad productiva (16 a 64 años) que padecen de diabetes tipo 1 o tipo 2 por más de 10 años, los cuales cuentan con el 50% a 60% de probabilidad de desarrollarla. Para identificar la condición médica bajo una observación física del órgano ocular se realiza un examen con lámpara de hendidura o una cámara de fondo de ojo con pupilas dilatadas [2]. Dichas formas de diagnóstico consumen tiempo y con frecuencia requieren una angiografía con fluoresceína o una tomografía de coherencia óptica para confirmar el grado de la retinopatía diabética.INTRODUCCIÓN ................................................................................................... 12 1. OBJETIVOS ....................................................................................................... 13 1.1 Objetivo General ........................................................................................... 13 1.2 Objetivos Específicos ................................................................................... 13 2. ANTECEDENTES .............................................................................................. 14 2.1 Retinopatía Diabética ................................................................................... 14 2.2 Fondo Ocular ................................................................................................ 15 2.3 Base de datos de retina ................................................................................ 16 3. METODOLOGÍA DEL PROCESAMIENTO DE IMÁGENES .............................. 20 3.1 Vasos Sanguíneos ........................................................................................ 20 3.1.1 Canal G con CLAHE adaptativo ........................................................... 21 3.1.2 Ajuste de intensidad ............................................................................. 24 3.1.3 Mínimo regional .................................................................................... 24 3.1.4 Reconstrucción morfológica ................................................................. 26 3.1.5 BottomHat............................................................................................. 28 3.1.6 Filtro de Gauss ..................................................................................... 29 3.1.7 Binarización .......................................................................................... 30 3.2 Disco óptico y fóvea ...................................................................................... 31 3.2.1 Disco óptico .......................................................................................... 31 3.2.2 Fóvea ................................................................................................... 36 3.3 Exudados ...................................................................................................... 38 3.3.1 Canal verde, filtro de media y ajuste de iluminación de fondo .............. 38 3.3.2 Binarización con Otsu adaptado y contraste adaptativo ....................... 40 - 7 - 3.3.3 Dilatación y reconstrucción morfológica por dilatación ......................... 40 3.4 Microaneurismas y hemorragias sanguíneas ............................................... 42 3.4.1 Ajuste de iluminación ............................................................................ 42 3.4.2 Mínimo regional y reconstrucción morfológica ...................................... 42 3.4.3 Segmentación de prelesiones y eliminación de vasos sanguíneos y fóvea................ .............................................................................................. 43 3.4.4 Dilatación y reconstrucción morfológica ............................................... 43 3.4.5 Binarización .......................................................................................... 43 4. CLASIFICACIÓN DE RETINOPATÍA ................................................................. 47 4.1 Clasificador ................................................................................................... 47 4.4.1 Máquinas de soporte vectorial (SVM) ................................................... 47 4.4.2 Red neuronal (ANN) ............................................................................. 49 4.2 Interfaz gráfica .............................................................................................. 50 4.3 Resultados .................................................................................................... 51 5. CONCLUSIONES .............................................................................................. 57 BIBLIOGRAFIA ...................................................................................................... 58 ANEXOS ................................................................................................................ 65PregradoDiabetic retinopathy is one of the most common conditions of diabetes [1], being the third cause of irreversible blindness in the world, but the first in people of productive age (16 to 64 years) who suffer from type 1 or type diabetes 2 for more than 10 years, which have a 50% to 60% chance of developing it. In order to identify the medical condition under a physical observation of the ocular organ, a slit lamp examination or a fundus camera with dilated pupils is performed [2]. These forms of diagnosis are time consuming and often require fluorescein angiography or optical coherence tomography to confirm the degree of diabetic retinopathy.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 2.5 ColombiaDiseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificialDesign of an automatic system to determine the degree of progression of diabetic retinopathy using artificial vision techniquesIngeniero MecatrónicoBucaramanga (Colombia)UNAB Campus BucaramangaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaPregrado Ingeniería Mecatrónicainfo:eu-repo/semantics/bachelorThesisTrabajo de Gradohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/redcol/resource_type/TPMechatronic EngineeringComputer visionArtificial intelligenceMedicineBiomedical engineeringApparatus and instrumentsInvestigationsAnalysisDiabetic retinopathyAutomationArtificial visionIngeniería mecatrónicaVisión por computadorInteligencia artificialMedicinaIngeniería biomédicaAparatos e instrumentosInvestigacionesAnálisisRetinopatía diabéticaAutomatizaciónVisión artificialAza Mantilla, Jessica Paola (2018). Diseño de un sistema automático para determinar del grado de progresión de retinopatía diabética utilizando técnicas de visión artificial. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNAB[1] FUNDACIÓN PARA LA DIABETES, CON LA COLABORACIÓN DE IER BAVIERA. Infografía "La retina. Órgano diana de la diabetes"[en línea]. 14 de noviembre, 2014. Disponible en: http://www.fundaciondiabetes.org/general/material/11/infografia-la-retina-organo-diana-de-la-diabetes[2] TYLER, Marshall E. y SAINE, Patrick J. Excerpted from: "Ophthalmic photography: retinal photography, angiography, and electronic imaging, 2nd edition" [en línea]. Butterworth-Heinemann Medical. Disponible en: http://www.opsweb.org/?page=fundusphotography[3] [25] ETIENNE Decencière, Xiwei Zhang, Guy Cazuguel, Bruno Lay, Béatrice Cochener, Caroline Trone, Philippe Gain, Richard Ordonez, Pascale Massin, Ali Erginay, Béatrice Charton, Jean-Claude Klein. Feedback on a publicly distributed database: the Messidor database. Image Analysis & Stereology, v. 33, n. 3, p. 231-234, Agosto. 2014. ISSN 1854-5165. Disponible en: http://www.ias-iss.org/ojs/IAS/article/view/1155[4] WANG, Huan. HSU, Wynne. GOH, Kheng Guan, LEE, Mong Li. "An Effective Approach to Detect Lesions in Color Retinal Images". Publicado online: National University of Singapore in 2000[5] HOOVER, Adam. KOUZNETSOVA, Valentina. GOLDBAUM, Michael. "Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter" Response. IEEE transactions on medical imaging, vol. 19, no. 3, march 2000[6] GRISAN, Enrico. PESCE, Alessandro. GIANI, Alfredo, FORACCHIA, Marco. RUGGERI, Alfredo. "A new tracking system for the robust extraction of retinal vessel structure" Proceedings of the 26th Annual International Conference of the IEEE EMBS. San Francisco, CA, USA • September 1-5, 2004[7] VALLABHA, Deepika. DORAIRAJ, Ramprasath. NAMUDURI, Kamesh. THOMPSON, Hilary. "Automated Detection and Classification of Vascular Abnormalities in Diabetic Retinopathy". At IEEE 2004[8] FRANKLIN, Sundararaj W; RAJAN, Samuelnadar E. "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images." Published in IET Image Processing, 2013. ZELJKOVIĆ, Vesna; BOJIC,[9] ZELJKOVIĆ, Vesna; BOJIC, Milena; TAMEZE, Claude; VALEV, Ventzeslav."Exudates detection and classification algorithm of diabetic patients’ retina images", Journal of Circuits, Systems, and Computers Vol. 22, No. 2 (2013) 1250087 (15 pages)[10] SHAHBEIG, Saleh. "Automatic and quick blood vessels extraction algorithm in retinal images." Published in IET Image Processing on 10th January 2013[11] WANG, Huan. HSU, Wynne. GOH, Kheng Guan, LEE, Mong Li. "An Effective Approach to Detect Lesions in Color Retinal Images." Publicado online: National University of Singapore in 2000[12] YUN, Wong Li. U. ACHARYA, Rajendra. Venkatesh, Y.V. Chee, Caroline c. MIN, Lim Choo. NG, E.Y.K. d. "Identification of different stages of diabetic retinopathyusing retinal optical images" Publicación en “Actualización de medicina de familia” [En línea] , URL: http://amf-semfyc.com/web/article_ver.php?id=1016[13] FADZIL, Ahmad; IZHAR, Lila Iznita; NUGROHO, Hermawan; NUGROHO, Hanung Adi. "Analysis of retinal fundus images for grading of diabetic retinopathy severity." Publicado online: 27 de Enero, 2011 en International Federation for Medical and Biological Engineering 2011[14] TARIQ, Anam; AKRAM, Usman; SHAUKAT, Arslan; KHAN, Shoab A. "Automated Detection and Grading of Diabetic Maculopathy in Digital Retinal Images." Publicado online: 17 de Enero, 2013 en Society for Imaging Informatics in Medicine 2013[15] OLOUMI, Faraz; RANGAYYAN, Rangaraj M.; ELLS Anna L. Computer-aided "Diagnosis of Proliferative Diabetic Retinopathy via Modeling of the Major Temporal Arcade in Retinal Fundus Images." Publicado online: 16 de Abril, 2013 en Society for Imaging Informatics in Medicine 2013[16] [17] FUNDACIÓN OFTALMOLÓGICA NACIONAL. "Retinopatía Diabética." Disponible en línea, URL: http://fon.org.co/[18] REVUELTA, Araceli Fernández. Especialista en Medicina Familiar y Comunitaria Profesora asociada. Universidad de Zaragoza Técnica de exploración del fondo de ojo. Publicación en “Actualización de medicina de familia” [En línea], URL: http://amf-semfyc.com/web/article_ver.php?id=1016[19] NHS DIABETIC EYE SCREENING PROGRAMME. "Diabetic Eye Screening Feature Based Grading Forms." Version 1.4, 1 November 201[20] DONAYRE, Dr. Juan Vásquez. Retinopatía Diabética (R.D.) "Oftalmología Médica I". [En línea], URL: http://sisbib.unmsm.edu.pe/bibvirtual/libros/medicina/cirugia/tomo_iv/oftal_med1.ht m[21] [22] RUDAS, Jorge. SÁNCHEZ Torres, Germán "Detección de patologías derivadas de las afecciones diabéticas: una revisión del análisis digital de imágenes de retina"[23] M. ORTEGA, M. G. Penedo, J. Rouco, N. Barreira, M. J. Carreira, "Retinal verification using a feature points based biometric pattern", EURASIP Journal on Advances in Signal Processing, vol. 2009, Article ID 235746, 13 pp., 2009.[24] A. Hoover, V. Kouznetsova and M. Goldbaum, "Locating Blood Vessels in Retinal Images by Piece-wise Threhsold Probing of a Matched Filter Response", IEEE Transactions on Medical Imaging , vol. 19 no. 3, pp. 203-210, March 2000.[26] FAUSETT, Laurene V. “Fundamentals of Neural Networks Architectures, Algorithms, and appliations”. Prentice-Hall (1994).[27] R. Santiago Montero, J. M. López Márquez, M. Ornelas Rodríguez. "Aplicando filtros de Gabor a segmentación de caracteres alfanuméricos en placas vehiculares." X Congreso Internacional sobre Innovación y Desarrollo Tecnológico, 23 al 25 de noviembre de 2011, Cuernavaca Morelos, México[28] KOVESI, Peter. "MATLAB and Octave Functions for Computer Vision and Image Processing. - What Are Log-Gabor Filters and Why Are They Good?" Centre for Exploration Targeting School of Earth and Environment The University of Western Australia. [En línea], URL: http://www.csse.uwa.edu.au/~pk/research/matlabfns[29] CUEVAS, Erik. ZALDIVAR, Daniel. PÉREZ, Marco. "Procesamiento digital de imágenes con Matlab y Simulink." 2010 Alfaomega Gurpo Editor S.A de C.[30] DUDA, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp. 11–15 (Enero, 1975[31] SUBBAN, R; KALIAPERUMAL, P; JAYASHREE, M; CAHNDRAN, R. “Eye retinal blood vessel segmentation methods” International Journal of Future Innovative Science and Technology, Mayo de 2015.[32] HALOI, Mrinal. “Improved micro-aneurysm detection using neural networks”. Indian Institute of Technology, Guwahati, Mayo de 2015[33] GAIKWARD, N; BADADAPURE, P. “Image processing technique for hard exudates detection for diagnosis of diabetic retinopathy” International Journal on recent and innovation trends in computing and communication, Abril de 2015.[34] CYBERNETICS GROUP, “Diabetic retinopathy image enhancement using CLAHE by programming TMS320C6416”. RV Colege Egineering.[35] YU, Ting; MA, Yide; LI, Weng. “Automatic detection of exudates and fovea for grading of diabetic macular edema in fundus image”. Lanzhou University, China.[36] AL-RAWI M., QUTAISHAT M., ARRAR M., "An improved matched filter for blood vessel detection of digital retinal images", Computers in Biology & Medicine, vol. 37, no. 2, pp. 262-267, 2007.[37] DOUGHERTY G., JOHSON M. J., WIERS M., "Measurement of retinal vascular tortuosity and its application to retinal pathologies", Journal of Medical & Biological Engineering & Computing, vol. 48, no. 1, pp. 87-95, 2010.[38] CHAUDHURI S., CHATTERJEE S., KATZ N., NELSON M., and GOLDBAUM M. "Detection of blood vessels in retinal images using two 114 dimensional matched filters", IEEE transactions on Medical Imaging, vol. 8, no. 3, pp. 263–269. 1989a[39] PATTON N., TARIQ M. A., MACGILLIVRAYD T., DEARYE I. J., BALJEAN D., ROBERT H. E., KANAGASINGAM Y., CONSTABLE J., "Retinal image analysis: Concepts, applications and potential", Progress in Retinal and Eye Research, vol. 25, no. 1, pp. 99-127, 2005[40] CHANG C. I., DU Y., WANG J., GUO S. M., THOUIN P. D., "Survey and comparative analysis of entropy and relative entropy thresholding techniques", IEE Proceedings of Vision, Image and Signal Processing, vol. 153, no. 6, pp. 837-850, 2006.[41] WU D., ZHANG M., LIU J. C., and BAUMAN W., "On the adaptive detection of blood vessels in retinal images", IEEE Transactions on Biomedical Engineering, vol. 53, no. 2, pp. 341-343, 2006.[42] AKITA K., KUGA H., "A computer method of understanding ocular fundus images", Pattern Recognition, vol. 15, no. 6, pp. 431–443, 1982.[43] SINTHANAYOTHIN C., KONGBUNKIAT V., PHOOJARUENCHANACHAI S., SINGALAVANIJA A., "Automated Screening System for Diabetic Retinopathy", Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis , pp. 915-920, 2003.[44] SINTHANAYOTHIN C., BOYCE J. F., WILLIAMSON T. H., COOK H. L., MENSAH E., LAL S., and USHER D., "Automated detection of diabetic retinopathy on digital fundus images", Diabetic medicine, vol. 19, no. 2, pp. 105-112, 2002.[45] WARD N., Tomlinson S., and Taylor C., "Image analysis of fundus photographs", Ophthalmology, vol. 96, no. 1, pp. 80-86, 1989[46] LIU Z. Q., CAI J., and BUSE R., "Hand-writing Recognition: Soft Computing and Probablistic Approaches", Springer Verlag, Berlin, 2003[47] WANG H., HSU W., GOH K. G., and LEE M., “An effective approach to detect lesions in colour retinal images“, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 181-187, 2000.[48] GARDNER G., Keating D., Williamson T. H., and Elliot A. T., “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool", British Journal of Ophthalmology, vol. 80, no. 11, pp. 940–944, 1996.[49] OSAREH A., Mirmehdi M., Thomas B., and Markham R., “Colour morphology and snakes for optic disc localization", Proceedings of 6th Conference on Medical Image Understanding and Analysis, pp. 21-24, 2002.[50] KANSKI, J J., Bowling, B., “Oftalmología Clínica“, Edición en español de la séptima edición de la obra original en inglés “Clinical Opthalmology. A systematic approach“. Elsevier, España.2012[51] LI Huiqi., and Opas C., “Automatic location of optic disc in retinal images‖, Proceedings of the International Conference on Image Processing", vol. 2, pp. 837-840, 2001.[52] LI Huiqi., and Opas C., “Automated Feature Extraction in Color Retinal Images by a Model Based Approach", IEEE Transactions Biomedical Engineering, vol. 51, no. 2, pp. 246-254, 2004[53] C. MARINO, E. Ares , M.G.Penedo, M. Ortega, N. Barreira, F. Gomez-Ulla, “Automated Three Stage Red Lesions Detection In Digital Color Fundus Images”, WSEAS Transactions on Computers, vol. 7, 2008, pp. 207-215.[54] SAI Prasad, Arpit Jain and Anurag Mittal, “Automated Feature Extraction for Early Detection of Diabetic Retinopathy in Fundus Images”, Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp.210-217.[55] BALASUBRAMANIAN, S., Pradhan Sandip and Chandrasekaran, V. “Red Lesion Detection in Digital Fundus Images“, Proc. International Conference on Image Processing, 2008, pp. 2932-2935.[56] GONZALES R. C., and Woods R. E., “Digital image processing”, 2nd Edition, Pearson Education, pp. 94-101, 2004.[57] USHER D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening”, Diabet Med 2004;21:84–90.[58] ZHENG Liu, Opas C, Krishnan SM. Automatic image analysis of fundus photograph. In: Proceedings of the International Conference on Engineering in Medicine and Biology, vol. 2. 1997. p. 524–5.[59] VARPA Group, VARIA DATABASE. Disponible en línea, URL: http://www.varpa.es/research/biometrics.htm.[60] GOLDBAUM, Michael M.D. U.S. National Institutes of Health. STARE DATABASE. Disponible en línea, URL: http://cecas.clemson.edu/~ahoover/stare/.[61] MSC MEDICAL IMAGING, UTRECHT UNIVERSITY. DRIVE DATABASE. Disponible en línea, URL: https://www.isi.uu.nl/Research/Databases/DRIVE/download.php.[62] KAUPPI, Tomi et al. DIARETDB DATABASE. Disponible en línea, URL: http://www2.it.lut.fi/project/imageret/diaretdb1_v2_1/[63] VEERANNA, Preethi. A Study On Diabetic Retinopathy Using Digital Image Processing Techniques. Gulbarga University. Disponible en línea, URL: http://hdl.handle.net/10603/137847[64] DECENCIÈRE E, et al. TeleOphta: Machine learning and image processing methods for teleophthalmology.IRBM (2013), Disponible en línea, URL: http://dx.doi.org/10.1016/j.irbm.2013.01.01[65] H. LI and O. Chutatape, “A model based approach for automated feature extraction in fundus images,” Proc. 9th IEEE International Conference on Computer Vision, Nice, France, October 2003.[66] ADARSH. P and D. Jeyakumari. Multiclass SVM-Based Automated Diagnosis of Diabetic Retinopathy. International conference on Communication and Signal Processing, April 3-5, 2013, IndORIGINAL2018_Tesis_Jesica_Paola_Aza.pdf2018_Tesis_Jesica_Paola_Aza.pdfTesisapplication/pdf2073543https://repository.unab.edu.co/bitstream/20.500.12749/1614/1/2018_Tesis_Jesica_Paola_Aza.pdfbde430a56584853bcee7bfc60dbe8af5MD51open accessTHUMBNAIL2018_Tesis_Jesica_Paola_Aza.pdf.jpg2018_Tesis_Jesica_Paola_Aza.pdf.jpgIM Thumbnailimage/jpeg4720https://repository.unab.edu.co/bitstream/20.500.12749/1614/2/2018_Tesis_Jesica_Paola_Aza.pdf.jpg7530ac4f63acc90e8b0cc0cd5cf2fe02MD52open access20.500.12749/1614oai:repository.unab.edu.co:20.500.12749/16142024-01-21 10:33:50.566open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.co |