Automatic detection and measurement of foveal avascular zone

Eladawy, Mohamed; Karawya, Sherif; Elbably, M.; Salem, Nancy M.

doi:10.1109/nrsc.2003.157359

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…The enlargement of FAZ has an effect that is apparent in colour-fundus images [78], which were previously seen in FFA images [77]. In the FFA image, the FAZ area has been determined utilising the Bayesian statistical techniques or thresholding methods dependent on the Sobel edge detectors as well as morphological operators [79]. The teams comprising H.A.…”

Section: Foveal Avasculture Zone (Faz) For Severe Level Of Diabetic Retinopathymentioning

confidence: 99%

A Wide-Ranging Review on Diabetic Retinotherapy

Soomro

Bughio

Siyal

et al. 2020

QUESTRJ

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Diabetic Retinopathy (DR) is one of the major eye diseases that causes damage to retina of the human eye ball due to the rupture of tiny blood vessels. DR is identified by the ophthalmologists on the basis of various specifications i.e., textures, blood vessels and pathologies. The ophthalmologists are recently considering software for eye diseases detection based on image processing designed by the computing techniques and bio-medical images. In the analysis of medical imaging, traditional techniques of image processing and computer vision have played an important role in the field of ophthalmology. From the past two decades, there is a tremendous advancement in the development of computerized system for DR detection. This paper comprises the five parts of analysis on image based retinal detection DR, named as review of low varying contrast techniques of the retinal fundus Images (RFI), review of noise effect in the fundus images, review of pathology detection method from the retinal fundus images, review of blood vessels extraction from the RFI, and review of automatic algorithm for the DR detection. This paper presents a comprehensive detail to each problem in the retinal images. The procedures that are currently utilized to analyze the contrast issue and noise issues are discussed in detail. The paper also explains the techniques used for segmentation. In the end, the recent automated detection system of related eye diseases or DR is described.

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Section: Foveal Avasculture Zone (Faz) For Severe Level Of Diabetic Retinopathymentioning

confidence: 99%

A Wide-Ranging Review on Diabetic Retinotherapy

Soomro

Bughio

Siyal

et al. 2020

QUESTRJ

View full text Add to dashboard Cite

show abstract

“…These retinal capillaries end at the surrounding edge of the FAZ and form a ring-like shape. FAZ has an average diameter of 500 to 600 µm and the physiological shape of the FAZ depends on the vascular pattern (Eladawy et al, 2003). In DR patients, the area of FAZ becomes larger due to the loss of the perifoveal retinal vessels (Ahmad Fadzil et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, FAZ is analysed based on the fundus fluorescein angiography (FFA) images (Eladawy et al, 2003;Haddouche et al, 2005). However, this method is invasive due to the injection of chemical dye into blood vessels.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of foveal avascular zone in digital colour retinal fundus images

Nugroho

Litasari

Listyalina

2017

IJBET

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One of the diabetes complications, diabetic retinopathy (DR), is characterised by the damage of retinal vessels, especially on the macular region. Located at the centre of retina and appeared as a cloudy dark spot in colour fundus image, macula is a fundamental area for high acumen of colour vision. Foveal avascular zone (FAZ) is located at the centre of macula and encircled by interconnected capillary beds. FAZ has a round or oval shape with an average diameter of 500-600 μm. In DR patients, the FAZ becomes larger due to the loss of perifoveal retinal vessels. In this study, a scheme for automated segmentation of FAZ in colour fundus images is proposed. The scheme consists of four stages: pre-processing, image enhancement, vessels segmentation and FAZ segmentation. Result shows that the average sensitivity, specificity and accuracy obtained are 80.86%, 99.17% and 97.49%. This indicates that the proposed scheme has successfully detected the FAZ.

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Image Projection Network: 3D to 2D Image Segmentation in OCTA Images

Chen

et al. 2020

IEEE Trans. Med. Imaging

105

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Optical coherence tomography angiography (OCTA) is a novel noninvasive imaging modality that allows micron-level resolution to visualize the retinal microvasculature. The retinal vessel segmentation in OCTA images is still an open problem, and especially the thin and dense structure of the capillary plexus is an important challenge of this problem. In this work, we propose a novel image magnification network (IMN) for vessel segmentation in OCTA images. Contrary to the U-Net structure with a down-sampling encoder and up-sampling decoder, the proposed IMN adopts the design of up-sampling encoding and then down-sampling decoding. This design is to capture more image details and reduce the omission of thin-and-small structures. The experimental results on three open OCTA datasets show that the proposed IMN with an average dice score of 90.2% achieves the best performance in vessel segmentation of OCTA images. Besides, we also demonstrate the superior performance of IMN in crossfield image vessel segmentation and vessel skeleton extraction.

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Automatic detection and measurement of foveal avascular zone

Cited by 3 publications

References 2 publications

A Wide-Ranging Review on Diabetic Retinotherapy

A Wide-Ranging Review on Diabetic Retinotherapy

Automated segmentation of foveal avascular zone in digital colour retinal fundus images

Image Projection Network: 3D to 2D Image Segmentation in OCTA Images

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