Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images
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 o...
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MDPI AG
2022-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/8/1334 |
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| author | Dora Elisa Alvarado-Carrillo Iván Cruz-Aceves Martha Alicia Hernández-González Luis Miguel López-Montero |
| author_facet | Dora Elisa Alvarado-Carrillo Iván Cruz-Aceves Martha Alicia Hernández-González Luis Miguel López-Montero |
| author_sort | Dora Elisa Alvarado-Carrillo |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.7067</mn></mrow></semantics></math></inline-formula>, Mean Distance to Closest Point of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.40</mn></mrow></semantics></math></inline-formula> pixels, and Hausdorff Distance of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27.96</mn></mrow></semantics></math></inline-formula> pixels, while demonstrating competitive results in terms of execution time (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.93</mn></mrow></semantics></math></inline-formula> s per image). |
| first_indexed | 2024-03-09T13:21:34Z |
| format | Article |
| id | doaj.art-97f397b8b6fb42feb797f7dee176f359 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T13:21:34Z |
| publishDate | 2022-04-01 |
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| spelling | doaj.art-97f397b8b6fb42feb797f7dee176f3592023-11-30T21:29:48ZengMDPI AGMathematics2227-73902022-04-01108133410.3390/math10081334Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus ImagesDora Elisa Alvarado-Carrillo0Iván Cruz-Aceves1Martha Alicia Hernández-González2Luis Miguel López-Montero3Center for Research in Mathematics (CIMAT), Guanajuato 36000, GTO, MexicoNational Council of Science and Technology (CONACYT)-Center for Research in Mathematics (CIMAT), Guanajuato 36000, GTO, MexicoHigh Specialty Medical Unit (UMAE), Specialties Hospital No. 1, Mexican Social Security Institute (IMSS), Leon 37320, GTO, MexicoHigh Specialty Medical Unit (UMAE), Specialties Hospital No. 1, Mexican Social Security Institute (IMSS), Leon 37320, GTO, MexicoThe 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.7067</mn></mrow></semantics></math></inline-formula>, Mean Distance to Closest Point of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.40</mn></mrow></semantics></math></inline-formula> pixels, and Hausdorff Distance of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27.96</mn></mrow></semantics></math></inline-formula> pixels, while demonstrating competitive results in terms of execution time (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.93</mn></mrow></semantics></math></inline-formula> s per image).https://www.mdpi.com/2227-7390/10/8/1334vessel segmentationmajor temporal arcadenumerical modelingretinal fundus imagesspline approximationweighted RANSAC |
| spellingShingle | Dora Elisa Alvarado-Carrillo Iván Cruz-Aceves Martha Alicia Hernández-González Luis Miguel López-Montero Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images Mathematics vessel segmentation major temporal arcade numerical modeling retinal fundus images spline approximation weighted RANSAC |
| title | Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images |
| title_full | Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images |
| title_fullStr | Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images |
| title_full_unstemmed | Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images |
| title_short | Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images |
| title_sort | robust detection and modeling of the major temporal arcade in retinal fundus images |
| topic | vessel segmentation major temporal arcade numerical modeling retinal fundus images spline approximation weighted RANSAC |
| url | https://www.mdpi.com/2227-7390/10/8/1334 |
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