Automatic localization and segmentation of Optic Disc in retinal fundus images through image processing techniques

GeethaRamani, R.; Dhanapackiam, C.

doi:10.1109/icrtit.2014.6996090

Cited by 11 publications

(2 citation statements)

References 16 publications

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“…Later, Noor et al extracted the desired area by cropping and separated 'Red', 'Green', 'Blue' color spaces of the retinal image [15]. Geetha et al extracted the desired region from green channel of processing RGB image by cropping to further improve the segmentation [16]. Ayushi et al extracted region of interest by passing the desired window across columns and rows for further processing [17] and Zilly et al performed pre-processing on input image using cropping and down sampling of retinal image followed by conversion of RGB to Lab color space and finally applied normalization to standardize the retinal images for further processing [18].…”

Section: Channel Separation [6]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: Channel Separation [6]mentioning

confidence: 99%

Pre-processing of Retinal Images for Removal of Outliers

Thakur

Juneja

2020

Wireless Pers Commun

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“…The Optic Disc (OD) is the brightest region of fundus image [27]. Since the OD has characteristics similar to the exudates, they may be falsely classified as lesions [28].…”

Section: Fundus Image and Messidor Databasementioning

confidence: 99%

Classification of visualization exudates fundus images results using support vector machine

Hassan¹,

Tahir²,

Zabidi³

et al. 2018

J. Fundam and Appl Sci.

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This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes.images (81 normal and 68 exudates) from MESSIDOR databas the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using SVM2 to determine its accuracy. pixels in fundus image using linear kernel function of SVM1 to diagnose DR. an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.Diabetic retinopathy (DR); Optic disc (OD); Support Vector Machine (SVM);

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Automatic Screening of Glaucomatous Optic Papilla Based on Smartphone Images and Patient’s Anamnesis Data

Camara

2019

Advances in Intelligent Systems and Computing

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Automatic localization and segmentation of Optic Disc in retinal fundus images through image processing techniques

Cited by 11 publications

References 16 publications

Pre-processing of Retinal Images for Removal of Outliers

Pre-processing of Retinal Images for Removal of Outliers

Classification of visualization exudates fundus images results using support vector machine

Automatic Screening of Glaucomatous Optic Papilla Based on Smartphone Images and Patient’s Anamnesis Data

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