Segmentation of Optic Disc from Fundus Images

Elbalaoui, Abderrahmane; Ouadid, Youssef; Fakir, Mohamed

doi:10.1109/iccse1.2018.8374223

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Recently, Soltani et al presented an approach for pre-processing of retinal image using gray scale conversion, histogram equalization, noise filtration using mean filtering [53]. Elbalaoui et al performed removal of vessels from retinal images by extracting the vessels using enhancement followed by thresholding, and replaced the vessel pixels with non-vessel neighbour pixels [54]. Jiang Y et al also performed extraction of vessels and impainted the same using fast marching to perform optimal segmentation [55].…”

Section: Hybrid Approaches [42]mentioning

confidence: 99%

Pre-processing of Retinal Images for Removal of Outliers

Thakur

Juneja

2020

Wireless Pers Commun

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Early diagnosis of diseases related with retina such as glaucoma is of utmost importance in current scenario as it is the second most prevailing cause of irreversible blindness over the world and is expected to increase further in near future. It is commonly diagnosed using retinal images which are acquired by digital fundus cameras. But the acquired images may be prone to certain outliers that create hindrance in diagnosis of glaucoma by tempering the accuracy. These outliers include retinal vessels, low contrast of images and uneven illumination that deteriorates the performance of disc and cup segmentation which are the key indicators to diagnose glaucoma. Thus, pre-processing of retinal images to remove outliers plays a significant role in diagnosis. This paper presents an approach for pre-processing the retinal fundus image followed by its comparison with state of the art. Based on the experimental analysis the performance of the proposed approach is found to be better than the state of the art based on the analysis using metrics such as peak signal to ratio, mean square error and structural similarity index. Further, the proposed approach has been compared with state of the art using metrics such as Jaccard index and dice similarity on the basis of segmentation outcomes on different pre-processing approaches.

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Section: Hybrid Approaches [42]mentioning

confidence: 99%

Pre-processing of Retinal Images for Removal of Outliers

Thakur

Juneja

2020

Wireless Pers Commun

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“…An average error of 7% on OD centre positioning is reached with no false detection. Vasanthi and group [13] proposed a model that presents an automated glaucoma detection method known as colour fundus imaging (CFI) and gradient vector flow (GVF) to extract OD boundary. To overcome the problem of over segmentation, Chan-Vese (C-V) model is proposed including local image information.…”

Section: Review Of Methodsmentioning

confidence: 99%

Untitled

2023

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER AND COMM

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Digital retinal images are widely used for early detection of retinal, ophthalmic and systemic diseases because they provide a non-invasive window to the human circularity system and associated pathologies. Automated retinal image analysis is becoming an important screening tool for early detection of certain risks and diseases like diabetic retinopathy, hypertensive retinopathy, age related macular degeneration, glaucoma etc. This in turn can be used to reduce human errors or to provide services to remote areas. Optic disc localization and segmentation in digital fundus images may seem an easy task, due to the fact that the optic disc appears in most of the images as the brightest spot, approximately circular. Hence a number of algorithms and techniques are being developed for its segmentation. This paper reviews and compares the algorithms and techniques anteriorly proposed in order to develop better and more efficacious techniques and algorithms for detection of optic disc and fovea.

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“…A plentiful of works have been proposed to segment OD and/or OC in fundus images, which can be mainly divided into traditional image processing based methods (Elbalaoui et al, 2018;Sarathi et al, 2016;Park et al, 2006) and recent deep learning based methods (Gao et al, 2020;Manjunath et al, 2020;Vismay et al, 2018). However, according to a recent study, deep learning techniques have dominating superiority on this OD/OC segmentation task (Veena et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection

He¹,

Li²,

Cai³

et al. 2022

Preprint

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Fundus photography has been routinely used to document the presence and severity of various retinal degenerative diseases such as age-related macula degeneration, glaucoma, and diabetic retinopathy, for which the fovea, optic disc (OD), and optic cup (OC) are important anatomical landmarks. Identification of those anatomical landmarks is of great clinical importance. However, the presence of lesions, drusen, and other abnormalities during retinal degeneration severely complicates automatic landmark detection and segmentation. Most existing works treat the identification of each landmark as a single task and typically do not make use of any clinical prior information. In this paper, we present a novel method, named JOINED, for prior guided multi-task learning for joint OD/OC segmentation and fovea detection. An auxiliary branch for distance prediction, in addition to a segmentation branch and a detection branch, is constructed to effectively utilize the distance information from each image pixel to landmarks of interest. Our proposed JOINED pipeline consists of a coarse stage and a fine stage. At the coarse stage, we obtain the OD/OC coarse segmentation and the heatmap localization of fovea through a joint segmentation and detection module. Afterwards, we crop the regions of interest for subsequent fine processing and use predictions obtained at the coarse stage as additional information for better performance and faster convergence. Experimental results reveal that our proposed JOINED outperforms existing state-of-the-art approaches on the publicly-available GAMMA, PALM, and REFUGE datasets of fundus images. Furthermore, JOINED ranked the 5th on the OD/OC segmentation and fovea detection tasks in the GAMMA challenge hosted by the MICCAI2021 workshop OMIA8.

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Segmentation of Optic Disc from Fundus Images

Cited by 9 publications

References 18 publications

Pre-processing of Retinal Images for Removal of Outliers

Pre-processing of Retinal Images for Removal of Outliers

Untitled

JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection

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