Color fundus image registration techniques and applications for automated analysis of diabetic retinopathy progression: A review

Saha, Sajib; Xiao, Di; Bhuiyan, Alauddin; Wong, Tien Yin; Kanagasingam, Yogesan

doi:10.1016/j.bspc.2018.08.034

Cited by 43 publications

(24 citation statements)

References 60 publications

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“…The process of changing the parameters of the transformation continues until the two images are optimal similar. The optimal transformation parameters T of the two images undergoing the registration process can be obtained by maximizing the similarity metric S, the following equation illustrates this process [ 29 ]: …”

Section: Related Workmentioning

confidence: 99%

Registration of computed tomography images of a lung infected with COVID-19 based in the new meta-heuristic algorithm HPSGWO

Dida

Charif

Benchabane

2022

Multimed Tools Appl

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Computed tomography (CT) helps the radiologist in the rapid and correct detection of a person infected with the coronavirus disease 2019 (COVID-19), and this by showing the presence of the ground-glass opacity in the lung of with the virus. Tracking the evolution of the spread of the ground-glass opacity (GGO) in the lung of the person infected with the virus needs to study more than one image in different times. The various CT images must be registration to identify the evolution of the ground glass in the lung and to facilitate the study and identification of the virus. Due to the process of registration images is essentially an improvement problem, we present in this paper a new HPSGWO algorithm for registration CT images of a lung infected with the COVID-19. This algorithm is a hybridization of the two algorithms Particle swarm optimization (PSO) and Grey wolf optimizer (GWO). The simulation results obtained after applying the algorithm to the test images show that the proposed approach achieved high-precision and robust registration compared to other methods such as GWO, PSO, Firefly Algorithm (FA), and Crow Searcha Algorithms (CSA).

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Section: Related Workmentioning

confidence: 99%

Registration of computed tomography images of a lung infected with COVID-19 based in the new meta-heuristic algorithm HPSGWO

Dida

Charif

Benchabane

2022

Multimed Tools Appl

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“…( 17 ), which utilized multiple instances of learning to produce a model built with under 5,000 fundus images and an area under the curve (AUC) of 0.79. Several studies focused on automated screening of DR and have achieved varying performance ( 18 , 19 ). Gulshan et al .…”

Section: Introductionmentioning

confidence: 99%

Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings

Bhuiyan¹,

Govindaiah²,

Alauddin³

et al. 2021

Ann Eye Sci

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Background: Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are among the leading causes of blindness in the United States and other developed countries. Early detection is the key to prevention and effective treatment. We have built an artificial intelligence-based screening system which utilizes a cloud-based platform for combined large scale screening through primary care settings for early diagnosis of these diseases. Methods: iHealthScreen Inc., an independent medical software company, has developed automated AMD and DR screening systems utilizing a telemedicine platform based on deep machine learning techniques. For both diseases, we prospectively imaged both eyes of 340 unselected non-dilated subjects over 50 years of age. For DR specifically, 152 diabetic patients at New York Eye and Ear faculty retina practices, ophthalmic and primary care clinics in New York city with color fundus cameras. Following the initial review of the images, 308 images with other confounding conditions like high myopia and vascular occlusion, and poor quality were excluded, leaving 676 eligible images for AMD and DR evaluation. Three ophthalmologists evaluated each of the images, and after adjudication, the patients were determined referrable or non-referable for AMD DR. Concerning AMD, 172 were labeled referable (intermediate or late), and 504 were non-referable (no or early). Concurrently, regarding DR, 33 were referable (moderate or worse), and 643 were non-referable (none or mild). All images were uploaded to iHealthScreen’s telemedicine platform and analyzed by the automated systems for both diseases. The system performances are tested on per eye basis with sensitivity, specificity, accuracy, and kappa scores with respect to the professional graders. Results: In identifying referable DR, the system achieved a sensitivity of 97.0% and a specificity of 96.3%, and a kappa score of 0.70 on this prospective dataset. For AMD, the sensitivity was 86.6%, the specificity of 92.1%, and a kappa score of 0.76. Conclusions: The AMD and DR screening tools achieved excellent performance operating together to identify two retinal diseases prospectively in mixed datasets, demonstrating the feasibility of such tools in the early diagnosis of eye diseases. These early screening tools will help create an even more comprehensive system capable of being trained on other retinal pathologies, a goal within reach for public health deployment.

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“…A detailed review [1] of fundus image registration techniques shows that feature-based approaches are more frequently applied to resolve this task. In [2], a graph matching method is combined with ICP to find correspondences between vascular bifurcations and register retinal vessels.…”

Section: Introductionmentioning

confidence: 99%

Imitation Learning Network for Fundus Image Registration Using a Divide-And-Conquer Approach

Bayer

Zhong

et al. 2020

Informatik Aktuell

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Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results. However, the results are difficult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result. The results show that the proposed registration network reduces the initial target registration error up to 95%.

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Color fundus image registration techniques and applications for automated analysis of diabetic retinopathy progression: A review

Cited by 43 publications

References 60 publications

Registration of computed tomography images of a lung infected with COVID-19 based in the new meta-heuristic algorithm HPSGWO

Registration of computed tomography images of a lung infected with COVID-19 based in the new meta-heuristic algorithm HPSGWO

Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings

Imitation Learning Network for Fundus Image Registration Using a Divide-And-Conquer Approach

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