Boundary Extraction and Tortuosity Calculation in Retinal Fundus Images

Manjunatha, R.; Koti, Mahesh; Sheshadri, H. S.

doi:10.1007/978-981-13-5802-9_96

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…4(a). Thus by retaining maximum and minimum Y-co-ordinate pixels at every X-co-ordinate, all along the length of the vessel, boundaries UB (upper boundary) and LB (lower boundary) are extracted [11] i.e. outlined as algorithm in Fig.…”

Section: Boundary Extraction and Segmentationmentioning

confidence: 99%

Boundary Extraction and Vessel Width Calculation in Retinal Fundus Images

Manjunatha¹,

Sheshadri²

2019

AJEAT

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A retinal vessel width measurement algorithm is presented towards ROP (Retinopathy of Prematurity) plus diagnostic automation. The algorithm involving geometrical feature extraction with the image processing is used to compute the effective width of the major vessels in a retinal image. Width measurement is shown to be a statistical parameter estimation related to the statistics of the retinal information. The algorithm is applied to the generic data bases available and the results are found to be satisfactory with ophthalmologist opinion. The effectiveness of the algorithm depends on the fundus image capturing settings.

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Section: Boundary Extraction and Segmentationmentioning

confidence: 99%

Boundary Extraction and Vessel Width Calculation in Retinal Fundus Images

Manjunatha¹,

Sheshadri²

2019

AJEAT

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“…The retinal image (RI) is pre-processed to make it ready for width analysis. The fundus images obtained by the Retcam is converted from RGB into gray scale (GI) image followed by AHE and binarization (BI) with dynamic threshold Dth (refer equation 1), details of the process are discussed elsewhere [3], [6]. -------------------1 III.…”

Section: Pre-processingmentioning

confidence: 99%

“…The vessel width is computed considering longer major vessel [6], [10]. A statistical approach as discussed in [6], [10], is applied to extract the major vessels.…”

Section: Major Vessel Extractionmentioning

confidence: 99%

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Retinal Image Vessel Width Analysis in Retinopathy of Prematurity

Manjunatha¹,

Sheshadri²

2019

AJCST

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This paper aims at development of Oscillating Pendulum Based Algorithm (OPBA) for retinal image width computation and analysis for the investigation of Retinopathy of prematurity (ROP). The algorithms have been explained in detail with theoretical investigations and simulations, images from local hospital data base were considered in the investigation. Further this paper also presents an overview of direct retinal vessel width computation method along with the comparative analysis. The results obtained are found to be encouraging for analysis of plus disease under ROP.

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“…The variations of retinal vessels and corneal nerves in ophthalmic images that may indicate several eye diseases and complications are commonly assessed for clinical decision support. For example, retina blood vessel tortuosity is a commonly seen symptom that results from plus disease in retinopathy of prematurity [1]. Among risk factors such as the retinal vessel tortuosity, density, foveal avascular zone (FAZ) area, and FAZ acircularity, statistically significant difference between no Diabetic Retinopathy (DR) and mild nonproliferative DR was observed only with the use of the tortuosity measure as shown in recent literature [2].…”

Section: Introductionmentioning

confidence: 96%

A Density and Reliability Guided Aggregation for the Assessment of Vessels and Nerve Fibres Tortuosity

Chen

Xie

et al. 2020

IEEE Access

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The tortuosity assessment of vessels and nerve fibres in ophthalmic images has drawn substantial attention, for its potentials in assisting various medical diagnoses. Numerous morphological tortuosity measures have been leveraged to quantify tortuosity from various perspectives, which warrants the simultaneous use of multiple measures with an aim to produce a robust and accurate assessment. This paper proposes an approach for the automated assessment of curvilinear structures' tortuosity. Starting with the generation of clusters of tortuosity density for each individual measure, labelled fuzzy sets are then extracted that enhances the readability of subsequent operations. Finally, results from multiple measures are aggregated by a nearest neighbour guided approach where weights are generated in a data-driven manner to explain the derived aggregations. Experimental results on both ophthalmic vessel and nerve images demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.

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Boundary Extraction and Tortuosity Calculation in Retinal Fundus Images

Cited by 3 publications

References 10 publications

Boundary Extraction and Vessel Width Calculation in Retinal Fundus Images

Boundary Extraction and Vessel Width Calculation in Retinal Fundus Images

Retinal Image Vessel Width Analysis in Retinopathy of Prematurity

A Density and Reliability Guided Aggregation for the Assessment of Vessels and Nerve Fibres Tortuosity

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