Localization of optic disc and fovea in retinal images using intensity based line scanning analysis

Kamble, Ravi; Kokare, Manesh; Deshmukh, Girish; Hussin, Fawnizu Azmadi; Mériaudeau, Fabrice

doi:10.1016/j.compbiomed.2017.04.016

Cited by 39 publications

(17 citation statements)

References 34 publications

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“…R ETINAL diseases are widespread among the population and early diagnosis is important for successful treatment. In recent years, many computer aided diagnosis (CAD) techniques based on the analysis of colour fundus images have been developed [34], [14], [15], [12], [20], [7]. However, due to the complex and varied nature of fundus images, there are still many problems that remained unsolved.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network

Xie

Liu

Cao

et al. 2021

IEEE Trans. Med. Imaging

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Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion. Although promising, using deep learning for fovea localisation also has many unsolved challenges. In this paper, we present a new end-to-end fovea localisation method based on a hierarchical coarse-to-fine deep regression neural network. The innovative features of the new method include a multi-scale feature fusion technique and a selfattention technique to exploit location, semantic, and contextual information in an integrated framework, a multi-field-of-view (multi-FOV) feature fusion technique for context-aware feature learning and a Gaussian-shift-cropping method for augmenting effective training data. We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components.

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Section: Introductionmentioning

confidence: 99%

End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network

Xie

Liu

Cao

et al. 2021

IEEE Trans. Med. Imaging

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“…Kamble Ravi et al [27] introduced a new approach for spotting OD and Fovea in which after preprocessing of image, one dimensional intensity analysis is performed where signal peak valley detection is used to locate OD in time domain and signal valley analysis is used to locate fovea in frequency domain. Framework evaluated on MESSIDOR dataset and achieved 99.75% accuracy for OD detection and 99.66% accuracy for fovea detection.…”

Section: Optic Disc (Od) Segmentationmentioning

confidence: 99%

A Survey on Automatic Diabetic Retinopathy Screening

Nage

Shitole

2021

SN COMPUT. SCI.

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Diabetes is a chronic disease caused due to the increase in the sugar level in the blood. Diabetes mainly affects heart, blood vessels, kidneys, eyes and nerves. There are mellitus type 1 and 2 diabetes, in which insulin is either not taken by the body or not created by the body. As per the statistics provided by WHO, about 422 million people worldwide are fighting with diabetes. Primary source of vision loss in the patients suffering from diabetes is diabetic retinopathy where the individual suffers from the damage and growth of abnormal blood veins in the retina. The disease is observed by ophthalmologist through identifying the presence of abnormalities starting from microaneurysms in the non-proliferative stage of DR and if these lesions' presence is ignored and not detected then it leads to the neovascularization in the proliferative stage which leads to unavoidable vision loss. DR can be cured if detected at the beginning stage. Manual method takes lots of time for detection of DR hence it is important to develop computer-based diagnostic system for DR detection using artificial intelligence (AI) and advance image processing to help ophthalmologists for spotting early symptoms of DR in less time. This paper provides a descriptive study about recent trends and technologies used for automatic spotting and grading of DR.

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“…Pereira et al [16] proposed an isotropic diffusionbased method which uses an ant colony algorithm for OD localization. Kamble et al [17] used a one-…”

Section: Optic Disk Detection Literaturementioning

confidence: 99%

“…Pereira et al [16] proposed an isotropic diffusion-based method which uses an ant colony algorithm for OD localization. Kamble et al [17] used a one-dimensional scanned density profile analysis for rapid and accurate localization of the OD and fovea. The proposed method effectively uses both time-and frequency-domain information for OD localization with high accuracy rates.…”

Section: Optic Disk Detection Literaturementioning

confidence: 99%

Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization

et al. 2019

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Accurate and efficient localization of the optic disk (OD) in retinal images is an essential process for the diagnosis of retinal diseases, such as diabetic retinopathy, papilledema, and glaucoma, in automatic retinal analysis systems. This paper presents an effective and robust framework for automatic detection of the OD. The framework begins with the process of elimination of the pixels below the average brightness level of the retinal images. Next, a method based on the modified robust rank order was used for edge detection. Finally, the circular Hough transform (CHT) was performed on the obtained retinal images for OD localization. Three public datasets were used to evaluate the performance of the proposed method. The optic disks were successfully located with the success rates of 100%, 96.92%, and 98.88% for the DRIVE, DIARETDB0, and DIARETDB1 datasets, respectively.

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Localization of optic disc and fovea in retinal images using intensity based line scanning analysis

Cited by 39 publications

References 34 publications

End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network

End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network

A Survey on Automatic Diabetic Retinopathy Screening

Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization

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