Segmentation of major temporal arcade in angiography images of retina using generalized hough transform and graph analysis

Nabi, Farshad; Yousefi, Hamed; Soltanian‐Zadeh, Hamid

doi:10.1109/icbme.2015.7404157

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The main application reported of this method is the detection of temporal arcade in retinal fundus images, where parabolas fit retina vessels [ 10 , 11 ]. Other reported works also have been focused on the human eye, for finding the iris [ 12 ], and in retina angiography [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Fast Parabola Detection Using Estimation of Distribution Algorithms

Guerrero-Turrubiates

Cruz-Aceves

Ledesma

et al. 2017

Computational and Mathematical Methods in Medicine

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This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.

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Section: Introductionmentioning

confidence: 99%

Fast Parabola Detection Using Estimation of Distribution Algorithms

Guerrero-Turrubiates

Cruz-Aceves

Ledesma

et al. 2017

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

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

et al. 2022

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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).

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Segmentation of major temporal arcade in angiography images of retina using generalized hough transform and graph analysis

Cited by 2 publications

References 15 publications

Fast Parabola Detection Using Estimation of Distribution Algorithms

Fast Parabola Detection Using Estimation of Distribution Algorithms

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

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