Automatic segmentation of optic disc using modified multi-level thresholding

Kankanala, Mila; Kubakaddi, Sanjeev

doi:10.1109/isspit.2014.7300575

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Mila et al initiated the processing of retinal image by removal of retinal vessels for proper segmentation. They applied match filter to convolve with input retinal image to intensify the retinal vessels and applied local entropybased threshold to delineate vessels followed by its removal using desired region as binary mask [44]. Similarly, with some new ideas Vaidya et al enhanced the contrast of image using intensity thresholding and adaptive histogram equalization along with piecewise linear and rotating structuring element for removal of vessels [45].…”

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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“…Sanjeev and Mila [17] proposed novel method for automatic segmentation of optic disc using a modified multithresholding technique on a preprocessed image. After sufficient preprocessing on the fundus image, segmentation was done using multi level thresholding.…”

Section: Review Of Methodsmentioning

confidence: 99%

Review: Segmentation of Fovea and Optic Disc

K¹,

K.V²,

K.R³

2017

International Journal of Advanced Research in Computer and Comm

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Abstract:Retinal image analysis is one of the crucial topics in medical image processing. During the last three decades, people are trying to extract the different features (like blood vessels, optic disk, macula, fovea etc.) automatically from retinalimage. Fovea and optic disc are the important features of a fundus retinal image.The quantitative analysis of retinal images is of increasing importance in the diagnosis of the various eye disorders.Hence a number of algorithms and techniques are being developed for its segmentation. This paper analyses various algorithms and techniques proposed earlier for the detection of optic disc and fovea.

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Segmentation of Optic Disc by Localized Active Contour Model in Retinal Fundus Image

Bhat

Kumar

2018

Smart Innovations in Communication and Computational Sciences

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Automatic segmentation of optic disc using modified multi-level thresholding

Cited by 5 publications

References 10 publications

Pre-processing of Retinal Images for Removal of Outliers

Pre-processing of Retinal Images for Removal of Outliers

Review: Segmentation of Fovea and Optic Disc

Segmentation of Optic Disc by Localized Active Contour Model in Retinal Fundus Image

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