Automatic Detection of Blood Vessel in Retinal Images Using Vesselness Enhancement Filter and Adaptive Thresholding

Fakir, Mohamed; Elbalaoui, Abderrahmane; Taifi, Khaddouj; Merbouha, Abdelkrim

doi:10.4018/ijhisi.2017010102

Cited by 6 publications

(2 citation statements)

References 21 publications

(19 reference statements)

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“…ThelocalizationoftheODisbasedonangledetectioninCSSmethod (Elbalaoui,2015).Therefore, alargenumberofvesselsinthecenteroftheODwillcreatealargenumberofangles.Yet,weare using these corners for localizing the OD center. In addition, blood vessels are detection using vesselnessfilter.Then,enhancementfilterisdesignedfromtheadaptivethresholdingoftheoutput of the vesselness filter for vessels detection (Elbalaoui, 2017). vessels are inpainted for accurate ODsegmentation.Finally,ODboundarydetectionusingactivecontourwithavariationallevelset formulation.Figure2showstheblockdiagramofODsegmentationwhichconsistsofthreephases, namely(1)ODlocalization,(2)vesselsremovaland(3)ODsegmentation.Figure2showstheblock diagramofODsegmentationwhichconsistsofthreephases,namely(1)ODlocalization,(2)vessels removaland(3)ODsegmentation.…”

Section: Methodsmentioning

confidence: 99%

Segmentation of Optic Disc in Fundus Images Using an Active Contour

Elbalaoui

Ouadid

Merbouha

2018

Journal of Electronic Commerce in Organizations

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A successful optic disc (OD) segmentation is an important task for automated detection white lesions related to diabetic retinopathy. Therefore, exudate detection is the authors' major purpose, but they must extract the OD prior to the process because it appears with similar color, intensity and contrast to other characteristics of the retinal image. The retinal image consists of blood vessels that emerge from the OD. The presence of these blood vessels may act as a disturbance for the detection of OD. This article presents a novel method for segmentation of the OD in retinal images. The methodology includes localization of the OD center, followed by elimination of vascular structure using an inpainting method. Finally, an active contour model was applied to boundary OD segmentation. The results are compared with a ground truth image from the ophthalmologist. The source retinal image for performing this work was obtained from the publicly available DRIVE and MESSIDOR databases. This method offers a successful segmentation of OD which may help in diagnosis of various retinal abnormalities.

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Section: Methodsmentioning

confidence: 99%

Segmentation of Optic Disc in Fundus Images Using an Active Contour

Elbalaoui

Ouadid

Merbouha

2018

Journal of Electronic Commerce in Organizations

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“…The OD is localised using Curvature Scale Space (CSS) [21]. 2D Vesselness function as described in [22] is used to eliminate blood vessels with which the pixels of blood vessels are replaced with neighbouring non-blood vessel pixels. Later, Local Binary Fitting [23] to obtain active contours of OD is applied.…”

Section: Introductionmentioning

confidence: 99%

Hard Exudate Extraction from Fundus Images using Watershed Transform

Satyananda

Narayanaswamy

Karibasappa

2019

IJEEI

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Diabetic Retinopathy is a medical condition which affects the eyes due to increased blood sugar levels. This is characterized by presence of exudatesdeposits of lipids in the posterior pole of the retina. If this ailment is not treated in earlier stages these deposits can cause blurred vision or even permanent blindness. This paper concentrates on extraction of hard exudates and optic disc from the retinal images of eyes using Marker based Watershed approach, which uses the minima imposition method to create mask and marker. The varying contrast across all the images has been taken care by a non-linear equation. Once these bright objects have been extracted from fundus images, area estimation is performed to eliminate the optic disk, thus retaining only exudates. These images have been procured from publicly available databases. Though software systems are easy to install, they prove to be expensive in terms of time and cost; thus this method has also been implemented on FPGA for an on-chip solution. The precision and sensitivity for exudate extraction sans optic disk are found to be 92.4% and 83.78% respectively. Though other techniques exist which provide better accuracy, the method described in this paper is found to be hardware friendly in comparison with other proven methods. Few steps of the algorithm developed are implemented on FPGA to provide an embedded system approach to this work, considering the advantages of a hardware-software combination.

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