Real-Life study of the numerical modeling of the upper and lower temporal arcades in retinal fundus images
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Keywords

análisis de fondo de retina
arcada temporal principal
modelado de curvas
funciones splines
pacientes sanos
retinopatía diab´ética
filtros gaussianos de correspondencia
segmentación binaria
modelos
diabetes
atención médica
medicina oftalmológica
salud fundus image analysis
major temporal arcade
curve modeling
spline functions
healthy patients
diabetic retinopathy
gaussian matched filters
binary segmentation
models
diabetes
medical care
ophtalmic medicine
healt

How to Cite

Rodríguez-Villalobos, Ángel J., Alvarado-Carrillo, D. E., Cruz-Aceves, I., Castellón-Lomelí, C. I., López-Montero, L. M., Hernández-González, M. A., & Giacinti, D. J. (2022). Real-Life study of the numerical modeling of the upper and lower temporal arcades in retinal fundus images. Nova Scientia, 14(28). https://doi.org/10.21640/ns.v14i28.2745

Abstract

Introduction: The high prevalence of Diabetes Mellitus type 2 in Mexico has positioned diabetic retinopathy as the main cause of blindness in adults of productive age in Mexico. Therefore, the timely detection of this disease is a priority task for the public health system. This article studies the efficiency of a new algorithm for determining the shape of the Major Temporal Arcade of the retina, using image segmentation techniques and numerical modeling of curves.                         

Method: The proposed methodology uses Gaussian Matched Filters that enhance the geometry of the blood vessels. Subsequently, the vascular structure is segmented by global thresholding of the enhanced image. Said segmentation is used as input to build a numerical model of the Superior and Inferior Temporal Arcades, using Spline functions.               

Results: The performance evaluation was carried out using 136 images of  pixels. The automatic retinal vein segmentation algorithm using the GMF method obtained an Accuracy of 0.9852; the numerical modeling algorithm gave a result of 6.01 pixels for the metric Mean Distance to the Closest Point (MDCP). Another previous study reported 12.33 pixels. Regarding time, the method reported an average time of 10.65 seconds per image.                       

Discussion: The proposed method was able to carry out the numerical modeling of temporal arches in fundus images efficiently. The results show that this method is an useful computational tool for the diagnosis of alterations in the anatomy of the eye.

https://doi.org/10.21640/ns.v14i28.2745
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References

Abràmoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., Pascale, M., Cochener, B., Gain, P., Tang, L., Lamard, M., Moga, D. C., Quellec, G. y Niemeijer, M. (2013). Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy. JAMA Ophthalmol, 131(3), 351-357. https://doi.org/10.1001/jamaophthalmol.2013.1743

Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N. y Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ digital medicine, 1(1), 1-8. https://doi.org/10.1038/s41746-018-0040-6

Abràmoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C. y Niemeijer, M. (2016). Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology & Visual Science, 57(13), 5200-5206. https://doi.org/10.1167/iovs.16-19964

Age-Related Eye Disease Study Research Group. (2001). A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins C and E, beta carotene, and zinc for age-related macular degeneration and vision loss: AREDS report no. 8. Archives of Ophthalmology, 119(10), 1417-1436. https://doi.org/ 10.1001/archopht.119.10.1417

Bahadar Khan, K., A-Khaliq, A. y Shahid, M. (2016). A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding. PloS One, 11(7), e0158996. https://doi.org/10.1371/journal.pone.0158996

Centers for Disease Control and Prevention. (2020). National diabetes statistics report.

Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M. y Goldbaum, M. (1989). Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging, 8(3), 263–269. https://doi.org/10.1109/42.34715

Cruz-Aceves, I., Cervantes-Sánchez, F. y Ávila-García, M. S. (2018). A novel multiscale Gaussian-matched filter using neural networks for the segmentation of X-ray coronary angiograms. Journal of Healthcare Engineering, 2018. https://doi.org/10.1155/2018/5812059

Cruz-Aceves, I., Guerrero-Turrubiates, J. y Sierra-Hernández, J. M. (2017). Parametric Object Detection Using Estimation of Distribution Algorithms. In S. Bhattacharyya, A. Mukherjee, I. Pan, P. Dutta y A. K. Bhaumik (Eds.). Hybrid intelligent techniques for pattern analysis and understanding (pp. 69-92). https://doi.org/10.1201/9781315154152

Ghanchi, F., Bailey, C. y Chakravarthy, U. (2013, July 14). Diabetic Retinopathy Guidelines. Retrieved from The Royal College of Ophthalmologists: https://www.rcophth.ac.uk/wp-content/uploads/2014/12/2013-SCI-301-FINAL-DR-GUIDELINES-DEC-2012-updated-July-2013.pdf

Giacinti, D. J., Cervantes Sánchez, F., Cruz Aceves, I., Hernández González, M. A. y López Montero, L. M. (2019). Determinación de la parábola de la vasculatura de la retina mediante un algoritmo computacional de segmentación. Nova Scientia, 11(23). https://doi.org/10.21640/ns.v11i23.1902

Mapayi, T., Viriri, S. y Tapamo, J. R. (2015). Comparative study of retinal vessel segmentation based on global thresholding techniques. Computational and mathematical methods in medicine. https://doi.org/10.1155/2015/895267

McAuliffe, M. J., Lalonde, F. M., McGarry, D., Gandler, W., Csaky, K. y Trus, B. L. (2001). Medical image processing, analysis, and visualization in clinical research. 14th IEEE Symposium on Computer-Based Medical Systems CBMS (pp. 381-386). IEEE.

Oloumi, F., Rangayyan, R. M. y Ells, A. L. (2012). Parabolic modeling of the major temporal arcade in retinal fundus images. IEEE Transactions on Instrumentation and Measurement, 61(7), 1825-1838. https://doi.org/10.1109/TIM.2012.2192339

Oloumi, F., Rangayyan, R. M. y Ells, A. L. (2013). Computer-aided diagnosis of proliferative diabetic retinopathy via modeling of the major temporal arcade in retinal fundus images. Journal of Digital Imaging, 26(6), 1124-1130. https://doi.org/10.1007/s10278-013-9592-9

Owens, D. R., Gibbins, R. L., Lewis, P. A., Wall, S., Allen, J. C. y Morton, R. (1998). Screening for diabetic retinopathy by general practitioners: ophthalmoscopy or retinal photography as 35 mm colour transparencies? Diabetic Medicine, 15(2), 170-175. https://doi.org/10.1002/(SICI)1096-9136(199802)15:2<170::AID-DIA518>3.0.CO;2-H

Sánchez, C. I. (2007). Parabola detection using Hough Transform. MATLAB Central File Exchange. Retrieved April 16, 2021, from https://www.mathworks.com/matlabcentral/fileexchange/15841-parabola-detection-using-hough-transform

Schumaker, L. (2007). Spline functions: basic theory. Cambridge University Press.

Shah, A., Lynch, S., Niemeijer, M., Amelon, R., Clarida, W., Folk, J., . . . Abràmoff, M. D. (2018). Susceptibility to misdiagnosis of adversarial images by deep learning based retinal image analysis algorithms. IEEE 15th International Symposium on Biomedical Imaging (pp. 1454-1457). IEEE. https://doi.org/10.1109/ISBI.2018.8363846

Tenorio, G. y Ramírez-Sánchez, V. (2010). Retinopatía diabética: conceptos actuales. Revista Médica Del Hospital General de México, (73), 193-201.

Teus, M. A., Arranz-Márquez, E., López-Guajardo, L. y Jiménez-Parras, R. (2007). Fondo de Ojo. Anales de Pediatría Continuada, 5(3), 163-166.

Valdez, S. I., Espinoza-Pérez, S., Cervantes-Sánchez, F. y Cruz-Aceves, I. (2018). Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection. En S. Bhattacharyya (Ed.). Hybrid Metaheuristics for Image Analysis (pp. 163-186). Springer. https://doi.org/10.1007/978-3-319-77625-5_7

World Health Organization. (2016). Global report on diabetes. World Health Organization.

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