Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images

Dora Elisa Alvarado-Carrillo, Iván Cruz-Aceves, Martha Alicia Hernández-González and Luis Miguel López-Montero
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Dora Elisa Alvarado-Carrillo: Center for Research in Mathematics (CIMAT), Guanajuato 36000, GTO, Mexico
Iván Cruz-Aceves: National Council of Science and Technology (CONACYT)-Center for Research in Mathematics (CIMAT), Guanajuato 36000, GTO, Mexico
Martha Alicia Hernández-González: High Specialty Medical Unit (UMAE), Specialties Hospital No. 1, Mexican Social Security Institute (IMSS), Leon 37320, GTO, Mexico
Luis Miguel López-Montero: High Specialty Medical Unit (UMAE), Specialties Hospital No. 1, Mexican Social Security Institute (IMSS), Leon 37320, GTO, Mexico

Mathematics, 2022, vol. 10, issue 8, 1-16

Abstract: The Major Temporal Arcade (MTA) is a critical component of the retinal structure that facilitates clinical diagnosis and monitoring of various ocular pathologies. Although recent works have addressed the quantitative analysis of the MTA through parametric modeling, their efforts are strongly based on an assumption of symmetry in the MTA shape. This work presents a robust method for the detection and piecewise parametric modeling of the MTA in fundus images. The model consists of a piecewise parametric curve with the ability to consider both symmetric and asymmetric scenarios. In an initial stage, multiple models are built from random blood vessel points taken from the blood-vessel segmented retinal image, following a weighted-RANSAC strategy. To choose the final model, the algorithm extracts blood-vessel width and grayscale-intensity features and merges them to obtain a coarse MTA probability function, which is used to weight the percentage of inlier points for each model. This procedure promotes selecting a model based on points with high MTA probability. Experimental results in the public benchmark dataset Digital Retinal Images for Vessel Extraction (DRIVE), for which manual MTA delineations have been prepared, indicate that the proposed method outperforms existing approaches with a balanced Accuracy of 0.7067 , Mean Distance to Closest Point of 7.40 pixels, and Hausdorff Distance of 27.96 pixels, while demonstrating competitive results in terms of execution time ( 9.93 s per image).

Keywords: vessel segmentation; major temporal arcade; numerical modeling; retinal fundus images; spline approximation; weighted RANSAC (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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