A Fast and Accurate Method for Glaucoma Screening from Smartphone-Captured Fundus Images

Mrad, Y.; Elloumi, Yaroub; Akil, Mohamed; Bedoui, Sammy

doi:10.1016/j.irbm.2021.06.004

Cited by 20 publications

(7 citation statements)

References 69 publications

(73 reference statements)

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“…On other hand, several automatic ophthalmological diagnostic systems such as [16], [17] and [18] expect sensitive segmentation of retinal vascular tree. In this context, our automated method can be directly employed to take benefit from its segmentation performance.…”

Section: Discussionmentioning

confidence: 99%

Extended U-net for Retinal Vessel Segmentation

Boudegga

Elloumi

Kachouri

et al. 2022

Advances in Computational Collective Intelligence

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The retinal vascular tree is an important biomarker for the diagnosis of ocular dis-ease, where an efficient segmentation is highly required. Recently, various standard Convolutional Neural Networks CNN dedicated for segmentation are applied for retinal vessel segmentation. In fact, retinal blood vessels are presented in different retinal image resolutions with a complicated morphology. Thus, it is difficult for the standard configuration of CNN to guarantee an optimal feature extraction and efficient segmentation whatever the image resolution is. In this paper, new retinal vessel segmentation approach based on deep learning architecture is propounded. The idea consists of enlarging the kernel size of convolution layer in order to cover the vessel pixels as well as more neighbors for extracting features. Within this objective, our main contribution consists of identifying the kernel size in correlation with retinal image resolution through an experimental approach. Then, a novel U-net extension is proposed by using convolution layer with the identified kernel size. The suggested method is evaluated on two public databases DRIVE and HRF having different resolutions, where higher segmentation performances are achieved respectively with 5*5 and 7*7 convolution kernel sizes. The average accuracy and sensitivity values for DRIVE and HRF databases are respectively in the order of to 0.9785, 0.8474 and 0.964 and 0.803 which outperform the segmentation performance for the standard U-net.

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Section: Discussionmentioning

confidence: 99%

Extended U-net for Retinal Vessel Segmentation

Boudegga

Elloumi

Kachouri

et al. 2022

Advances in Computational Collective Intelligence

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“…Also, this method is the first attempt to differentiate fundus images into normal, dry AMD, and wet AMD classes. Furthermore, this suggested contribution is able to be implemented into a mobile End-To-End system for retinal pathology screening [28], [29].…”

Section: Discussionmentioning

confidence: 99%

Automated Diagnosis of Retinal Neovascularization Pathologies from Color Retinal Fundus Images

Boukadida¹,

Elloumi²,

Kachouri³

et al. 2022

Advances in Computer Graphics

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The retinal Neo-Vascularization (NV) is the abnormal growth of new blood vessels in the retina, which leads to a severe reduction on visual acuity and blindness. It is a main biomarker to screening several diseases, where the Proliferative Diabetic Retinopathy (PDR) and Wet Age-related Macular Degeneration (WAMD) are the most common ones. The NV severity requires a fast screening to avoid severe degradation. However, it is labor intensive and timeconsuming for the ophthalmologists. In this paper, we suggest an automated screening method that automatically detects NV from fundus photography and classify it as PDR, WAMD and Healthy. For this purpose, the image is preprocessed and then provided a transfer learned model of the VGG16 neural network. The method was evaluated using a dataset containing 395 fundus photographs of retinal images where an accuracy of 98.30%, a sensitivity of 98.66%, a specificity of 98.33% were achieved. In addition, the areas under curve in terms of classes were between 98% and 100%.

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“…In recent years, various intelligent algorithms and neural network models have been widely used in image superresolution reconstruction [15]. These methods can be modeled using image databases or images themselves [16]. Designing learning strategies and training the learning ability of models to actively learn or find the relevant information between high-resolution images and low-resolution images is necessary.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Image Processing Technology in New Media Short Video Quality Improvement in Film and Television Postproduction

Wang

2023

International Journal of Digital Multimedia Broadcasting

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With the development of film and television industry and the rise of new media short video, film and television postproduction process has higher and higher requirements for image quality. In film and television postproduction, optimizing the image quality can enhance the resolution and make the image more vivid and detailed. High-quality image can fully embody the value of film and television, as well as promote the development of new media short videos. This paper optimizes image quality by improving image processing technology, thus improving the quality and value of film and television and new media short videos. In this paper, a convolutional neural network combined with a nonlinear activation function is used to establish an improved image processing technology model to efficiently extract image features. This technology can enhance the ability to extract image features, improve the accuracy of image feature extraction, and then improve the image resolution and details, thus improving the image quality in the process of film and television postproduction. The results show that the average value of PSNR is 30.29. The average value of PSNR of the proposed algorithm is higher than that of other algorithms, indicating that the error between the image processed by the proposed algorithm and the original image is small. The average SSIM of the algorithm in this paper is 0.903, which is closer to 1. Compared with other algorithms, the structure processed by the algorithm in this paper is more similar to the original structure, resulting in a better graph. The algorithm in this paper has the best performance on both the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM). The improved image processing technology proposed in this paper can effectively improve the accuracy of image feature extraction, making film and television or new media short video images of higher quality.

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A Fast and Accurate Method for Glaucoma Screening from Smartphone-Captured Fundus Images

Cited by 20 publications

References 69 publications

Extended U-net for Retinal Vessel Segmentation

Extended U-net for Retinal Vessel Segmentation

Automated Diagnosis of Retinal Neovascularization Pathologies from Color Retinal Fundus Images

Application of Improved Image Processing Technology in New Media Short Video Quality Improvement in Film and Television Postproduction

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