Exudates detection in fundus images using mean-shift segmentation and adaptive thresholding

Elbalaoui, Abderrahmane; Fakir, Mohamed

doi:10.1080/21681163.2018.1463175

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…The different efficiencies of the allocated difference algorithm can be evaluated by measuring the quality of the candidate solutions, so as to generate the optimal solution for the population.Reference [11] proposes a new type of active contour model that combines regional information and image edge information to achieve image segmentation. And the divergence operator is used to balance the information of all aspects of the image, which enhances the adaptability of the model.Reference [12] proposed an automatic method for detecting exudate in digital fundus images. It mainly includes four steps: color shift correction, disc elimination, exudate segmentation, and separation of exudate and background.…”

Section: Introductionmentioning

confidence: 99%

Nucleus Image Segmentation Method Based on GAN Network and FCN Model

Zhang

Shi

Chen

et al. 2021

Preprint

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Aiming at the problems of rough edges and low accuracy in processing cell nucleus image segmentation in existing image segmentation methods. A cell nucleus image segmentation technology based on generative adversarial network (GAN) network and fully convolutional network (FCN) model is proposed. First, the FCN model is used to perform preliminary segmentation of the cell nucleus image, in which the fully connected layer convolution and skip connection are used to improve the accuracy of image segmentation. Then, improve the GAN network, introduce splitting branches into the discriminator structure, and combine the GAN network and the splitting network into one. At the same time, pixel loss is introduced in the generator to obtain a nucleus image that is visually more similar to the real image. Finally, the segmented image output by the FCN model is used as the input of the GAN network to achieve high-precision segmentation of the nucleus image. The proposed method is experimentally demonstrated based on the 2018 data science bowl dataset. The results show that it can achieve rapid convergence, and the mean intersection over union (MIoU) is 85.34%, which is better than other comparison methods.

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Section: Introductionmentioning

confidence: 99%

Nucleus Image Segmentation Method Based on GAN Network and FCN Model

Zhang

Shi

Chen

et al. 2021

Preprint

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“…In medical system, Diabetic Retinopathy is the significant topic in the medical image processing to detect the affected region and exudates. Over 438 million people with diabetes will be found worldwide by 2030 as per the World Diabetes Foundation (Elbalaoui et al, 2019). According to (Gilbert et al, 2020), 65 million adults are affected by diabetic mellitus only in India and is expected to rise count to 130 million by 2045.…”

Section: Introductionmentioning

confidence: 99%

Recent Automated Hard Exudates Detection Systems i n Diabetic Retinopathy

S. Gorde,

A. Gurjar

2021

NVEO

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Diabetic Retinopathy (DR) is the complexity arises because of raise in glucose level in the blood that causes retinal damage in diabetic patient's eye and leads to vision loss if not early detected and treated. Due to DR, retinal blood vessels get damaged and accumulation of lipoprotein forms Hard Exudates (HE). The early detection of HE in fundus photographs with the use of computer assisted tools helps Ophthalmologists to analyze and prevent vision impairment. Therefore many computer assisted systems were developed using image processing, supervised and unsupervised machine learning and deep learning. In this review, an analysis of latest development of such systems to detect hard exudates is presented. The objective of this paper is to define that systems which uses deep learning algorithms for efficient detection of bright lesions as HEs.

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“…For the computer assisted recognition, a hybrid algorithm using Migrating Bird Optimisation and Support Vector Machine (MB-SVM) classifiers has been proposed [25]. A four steps strategy for the detection was devoleped by Elbalaoui and Fakir [26]. It includes shifting the colour space, Optic Disc (OD) elimination, exudates segmentation and finally separation of exudates from the background.…”

Section: Introductionmentioning

confidence: 99%

Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

Pradeep¹,

Joseph²

2020

IJECE

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Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.

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Exudates detection in fundus images using mean-shift segmentation and adaptive thresholding

Cited by 10 publications

References 26 publications

Nucleus Image Segmentation Method Based on GAN Network and FCN Model

Nucleus Image Segmentation Method Based on GAN Network and FCN Model

Recent Automated Hard Exudates Detection Systems i n Diabetic Retinopathy

Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

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