Robust intensity variation and inverse surface adaptive thresholding techniques for detection of optic disc and exudates in retinal fundus images

Karkuzhali, S.; Manimegalai, D.

doi:10.1016/j.bbe.2019.07.001

Cited by 13 publications

(14 citation statements)

References 34 publications

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“…It contains a participation grid m of size 7 x N. The lattice component m_ij addresses participation of the jth information Y_j to class I. The accompanying advances are performed to identify exudates in macula area [17][18][19][20][21][22][23][24]:…”

Section: Identi Cation Of Exudates In Macula Regionmentioning

confidence: 99%

WITHDRAWN: Identification and Prediction of Diabetic Macula Edema in Two-dimensional Fundus Images Using Raphe Assessment and Deep Convolution Neural Networks

Karkuzhali¹,

Manimegalai

2022

Preprint

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Diabetic Macula Edema (DME) is brought about by collection of extracellular liquid from hyper penetrable vessels inside the macula. DME is one of the main sources of visual impairment among diabetic patients. The patients who are influenced by diabetes and Diabetic Retinopathy (DR) are well on the way to be influenced by DME. The presence of Exudates in macula area is the significant sign of essence of DME. The Optic Disc (OD) and Blood Vessels (BVs) are the retinal milestones divided to find macula and fovea locale. Area of macula focus is vital for portion the anomalies in the macula locale. The irregularity and its seriousness of the sickness in macula district can be distinguished solely after finding the macula place. Pictures from High Resolution Fundus (HRF) database are utilized for testing and examination. The Atanassov’s Intuitionistic fuzzy Sets (A-FIS) and morphological tasks are utilized to portion OD and BVs. With these retinal landmarks, macula is recognized by raphe assessment. The Exudates (EXs) present in macula district are portioned proficiently by inclination based versatile thresholding. Deep Convolution neural network used for classification of DME and non-DME affected images. The division of OD and EXs has accomplished SE of 95.21%, 98.2%, SPE of 82.36%, 97.4% and ACC of 99.21%, 99.23% separately.

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Section: Identi Cation Of Exudates In Macula Regionmentioning

confidence: 99%

WITHDRAWN: Identification and Prediction of Diabetic Macula Edema in Two-dimensional Fundus Images Using Raphe Assessment and Deep Convolution Neural Networks

Karkuzhali¹,

Manimegalai

2022

Preprint

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“…A collection of 100 DR-affected retinal images was gathered at Bejan Singh Eye Hospital in Nagarcoil, India, using a special fundus camera and scanned with a laser film scanner. It is used by [ 84 ] for exudate and optic disc detection.…”

Section: Databasementioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

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“…Al-Bander et al [28] proposed Multiscale sequential CNN to detect fovea and the optic disc. Karkuzhali et al [29] proposed adaptive thresholding to detect the optic disc.…”

Section: Introductionmentioning

confidence: 99%

Exudate Segmentation for Diabetic Retinopathy Using Modified FCN-8 and Dice Loss

2022

IJIES

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The severity of diabetic retinopathy can lead to blindness if undiagnosed and untreated. The presence of hard exudates is one of the diabetic retinopathy symptoms. Therefore, automatic segmentation of hard exudates can provide an important diagnosis for diabetic retinopathy. Due to the relatively small dimensions of the exudates and the availability of the optic disc that has similar color, the exudate segmentation is a challenge in itself. In this study, we propose a modification of the fully convolutional network model (FCN-8) by combining FCN-8 and shortcuts to improve the performance of FCN-8. Each shortcut consists of a convolutional layer and batch normalization to reduce input degradation. Prior to processing the hard exudates using a modified FCN-8, the optic disc was removed from the retinal image by detecting the area using Faster R-CNN based on the Alexnet architecture. For training and testing, we applied the IDRiD dataset to evaluate the performance of our proposed architecture. Experiments show that our proposed architecture provides accuracy, sensitivity, specificity of 98.18 %, 81.7%, and 98.37 % respectively. Our proposed method gives higher sensitivity compared to Autoencoder, U-Net,

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Robust intensity variation and inverse surface adaptive thresholding techniques for detection of optic disc and exudates in retinal fundus images

Cited by 13 publications

References 34 publications

WITHDRAWN: Identification and Prediction of Diabetic Macula Edema in Two-dimensional Fundus Images Using Raphe Assessment and Deep Convolution Neural Networks

WITHDRAWN: Identification and Prediction of Diabetic Macula Edema in Two-dimensional Fundus Images Using Raphe Assessment and Deep Convolution Neural Networks

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Exudate Segmentation for Diabetic Retinopathy Using Modified FCN-8 and Dice Loss

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