Automatic Extraction of the Retina AV Index

Caderno, I. G.; Penedo, Manuel G.; Mariño, C.; Carreira, Marı́a J.; Gómez‐Ulla, Francisco; González, Francisco

doi:10.1007/978-3-540-30126-4_17

Cited by 10 publications

(9 citation statements)

References 5 publications

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“…The methodology allows to tune several parameters to apply such filters as for creases with a concrete width range or crease length. In Caderno et al (2004) a methodology was presented for automatic parameter tuning by analyzing contrast variance in the retinal image.…”

Section: Feature Points Extractionmentioning

confidence: 99%

Retinal vessel tree as biometric pattern

Ortega¹,

Penedo²

2011

Biometrics

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Section: Feature Points Extractionmentioning

confidence: 99%

Retinal vessel tree as biometric pattern

Ortega¹,

Penedo²

2011

Biometrics

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“…Note that fitting the interior of the vessels has been the most common technique used [14,15]. However, this strategy has some disadvantages.…”

Section: Alonso-montes Et Almentioning

confidence: 99%

Fast retinal vessel tree extraction: A pixel parallel approach

Alonso-Montes

Vilariño

Dudek

et al. 2008

Circuit Theory & Apps

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SUMMARYEarly ocular disease diagnosis is an important field in medical research. From the image processing point of view, many strategies and algorithms have been developed to deal with the extraction of the retinal vessel tree. Although reliable and accurate results have been obtained, the main disadvantage in most of these proposals is the high computation effort required. In this paper, a methodology to extract the retinal vessel tree has been developed and tested in a fine-grain pixel-parallel processor array. An analysis of the execution time has been made to demonstrate its capabilities regarding the computation speed. Moreover, an analysis of the accuracy using a publicly available database has been made to validate the algorithm performance.

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“…A lower AVR value is associated with a high blood pressure increasing the risk of stroke, diabetes and hypertension. Currently, most of the the medical applications developed to compute the AVR ratio from retinal images are semi-automatic approaches [6], [7], since a manual classification of both types of vessels (vein and artery) should be made in order to compute the AVR ratio.…”

Section: Introductionmentioning

confidence: 99%

Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach

Alonso-Montes

Penedo

Vilariño

2008

2008 11th International Workshop on Cellular Neural Networks and Their Applications

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The study of blood vessel features plays an important role in order to characterise markers used in early disease diagnosis. The arteriolar-to-venular (AVR) diameter ratio is an earlier marker related with cardiovascular risk, hypertension and diabetes. The extraction of the retinal vessel tree is not only the main task related with those medical applications intended to compute the AVR ratio, but it also implies a high computation effort. From the image processing point of view, many strategies and algorithms have been developed to deal with the extraction of this retinal vessel tree but specially regarding on the accuracy, but the execution time is still an open problem. In this paper, a methodology to extract the retinal vessel tree, tested in a fine-grain pixel-parallel processor array, is integrated into an application for the estimation of the AVR ratio in angiographies.

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Automatic Extraction of the Retina AV Index

Cited by 10 publications

References 5 publications

Retinal vessel tree as biometric pattern

Retinal vessel tree as biometric pattern

Fast retinal vessel tree extraction: A pixel parallel approach

Arteriolar-to-venular diameter ratio estimation: A pixel-parallel approach

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