Optic Disc Segmentation using Nonconvex Rank Approximation from Retinal Fundus Images

Lenka, Satyabrata; Mayaluri, Zefree Lazarus

doi:10.1109/isssc56467.2022.10051542

Cited by 2 publications

(1 citation statement)

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“…One of our previous works [14] that proposed for OD segmentation using robust PCA and using this method, glaucoma detection has been done successfully with four classifiers.…”

Section: Methods For Od and Oc Segmentationmentioning

confidence: 99%

Hybrid glaucoma detection model based on reflection components separation from retinal fundus images

Mayaluri

Lenka

2023

EAI Endorsed Trans Perv Health Tech

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The diagnosis of diseases associated to the retina is significantly aided by retinal fundus images. However, when flash illumination is used during image acquisition, specularity reflection can occur on images. The retinal image processing applications are popular now days in diseases detection such as glaucoma, diabetic retinopathy, and cataract. Many modern disease detection algorithms suffer from performance accuracy limitation due to the creation of specularity reflection problem. This research proposes a hybrid model for screening of glaucoma which includes a preprocessing step to separate specular reflections from corrupted fundus images, a segmentation step using modified U-Net CNN, a feature extraction step, and an image classification step using support vector machine (SVM) with different kernels. Firstly, the diffuse and specular components are obtained using seven existing methods and apply a filter having high emphasis with a function called similar in each component. The best method, which provides highest quality images, is chosen among the seven compared methods and the output image is used in next steps for screening of glaucoma. The experimental results of the proposed model show that in preprocessing step, maximum improvement in terms of PSNR and SSIM are 37.97 dB and 0.961 respectively. For glaucoma detection experiment the results have the accuracy, sensitivity, and specificity of 91.83%, 96.39%, and 95.37% respectively and AUROC of 0.971.

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“…One of our previous works [14] that proposed for OD segmentation using robust PCA and using this method, glaucoma detection has been done successfully with four classifiers.…”

Section: Methods For Od and Oc Segmentationmentioning

confidence: 99%