Submit to Special Issue Submit Abstract to Special Issue Review for J. Imaging Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advances in Retinal Image Processing

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 11958

Share This Special Issue

Special Issue Editors

Dr. P. Jidesh

E-Mail Website
Guest Editor

Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, Karnataka 575 025, India
Interests: mathematical imaging; image processing; data compression; graph image processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Vasudevan (Vengu) Lakshminarayanan

E-Mail Website
Guest Editor

Theoretical and Experimental Epistemology Laboratory, University of Waterloo, Waterloo, ON N2J 4A8, Canada
Interests: vision science; physics; ECE and systems design engineering; optics and photonics; including mathematical methods; waveguides and fiber optics; image processing; biomedical optics; deep learning/machine learning in ophthalmic diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Visual impairment is a primary global challenge in the present era. Lack of awareness, shortage of resources and trained personnel, inability to seek immediate medical treatments, etc. can lead to several retinal disorders, which can in turn lead to blindness or severe visual impairment. The human retina is examined through non-invasive procedures such as fundus photography and optical coherence tomography. Other methods can include fluorescein angiography. From these images of the retina, ophthalmologists visually analyze and locate the retinal abnormalities of various retinal disorders. However, this is not feasible due to large numbers of patients, lack of adequately trained clinical personnel, as well as resources in the developing world, and underdeveloped or underserved areas in the developed world.

Automated retinal image analysis, which can be used in teleophthalmology, is thus of utmost importance to diagnose and grade, as well as monitor the progression or regression the disease after surgical and therapeutic intervention. State-of-the art devices such as portable OCT, smart-phone camera attachments, etc. have simplified the acquisition of retinal images to some extent. Nevertheless, the ever-increasing blind population and the availability of massive computational resources have spurred the urgent need to develop automated retinal imaging applications. The gamut of cutting-edge technologies such as Artificial Intelligence and deep learning is a possible gateway to resolve these challenges. The domain of enhancement and registration of retinal images, multimodal analysis, and multiple disorder detection, as well as vendor-independent retinal image processing, are the limelight of retinal imaging.

A first volume of the Special Issue titled “Frontiers in Retinal Image Processing” (https://www.mdpi.com/journal/jimaging/special_issues/retinal_image_processing) was published in the journal. Five selected papers were published in the Special Issue. The second volume of the Special Issue is now being planned to be published in the journal. The Special Issue aims at research, broadly defined, that deals with multiple issues, all orbiting around image acquisition and processing, which can be of assistance to the clinician and ophthalmic manufacturers. The objective of this issue is to gather in one venue relevant high-quality research and thereby contribute to the field of medical imaging and image processing in ophthalmology.

Topics of Interest:

The topics of interest include (but not limited to):

Automatic retinal disorder classification from retinal images
Early-stage diagnosis and grading of retinal disorders
Handheld or computerized devices for retinal image acquisition
Restoration and enhancement of retinal images
Analysis of retinal disorders using multimodal retinal images
Segmentation of retinal images
Image registration
Computer-vision-based retinal image analysis
Volumetric analysis of retinal images using image processing techniques
Analysis of progressive retinal disorders using machine learning and deep learning
Cross-vendor supported applications to assist ophthalmologists
Multispectral retinal image analysis and applications

Dr. P. Jidesh
Prof. Dr. Vasudevan Lakshminarayanan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

retinal image processing
ophthalmology
classification
segmentation
registration
denoising
retinal disorders

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

12 pages, 5286 KiB

Open AccessArticle

Subjective Straylight Index: A Visual Test for Retinal Contrast Assessment as a Function of Veiling Glare

by Francisco J. Ávila, Pilar Casado, Mª Concepción Marcellán, Laura Remón, Jorge Ares, Mª Victoria Collados and Sofía Otín

J. Imaging 2024, 10(4), 89; https://doi.org/10.3390/jimaging10040089 - 10 Apr 2024

Viewed by 387

Abstract

Spatial aspects of visual performance are usually evaluated through visual acuity charts and contrast sensitivity (CS) tests. CS tests are generated by vanishing the contrast level of the visual charts. However, the quality of retinal images can be affected by both ocular aberrations and scattering effects and none of those factors are incorporated as parameters in visual tests in clinical practice. We propose a new computational methodology to generate visual acuity charts affected by ocular scattering effects. The generation of glare effects on the visual tests is reached by combining an ocular straylight meter methodology with the Commission Internationale de l’Eclairage’s (CIE) general disability glare formula. A new function for retinal contrast assessment is proposed, the subjective straylight function (SSF), which provides the maximum tolerance to the perception of straylight in an observed visual acuity test. Once the SSF is obtained, the subjective straylight index (SSI) is defined as the area under the SSF curve. Results report the normal values of the SSI in a population of 30 young healthy subjects (19 ± 1 years old), a peak centered at SSI = 0.46 of a normal distribution was found. SSI was also evaluated as a function of both spatial and temporal aspects of vision. Ocular wavefront measures revealed a statistical correlation of the SSI with defocus and trefoil terms. In addition, the time recovery (TR) after induced total disability glare and the SSI were related; in particular, the higher the RT, the greater the SSI value for high- and mid-contrast levels of the visual test. No relationships were found for low contrast visual targets. To conclude, a new computational method for retinal contrast assessment as a function of ocular straylight was proposed as a complementary subjective test for visual function performance. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

► Show Figures

Figure 1

13 pages, 2961 KiB

Open AccessArticle

When Sex Matters: Differences in the Central Nervous System as Imaged by OCT through the Retina

by Ana Nunes, Pedro Serranho, Pedro Guimarães, João Ferreira, Miguel Castelo-Branco and Rui Bernardes

J. Imaging 2024, 10(1), 6; https://doi.org/10.3390/jimaging10010006 - 25 Dec 2023

Viewed by 1388

Abstract

Background: Retinal texture has gained momentum as a source of biomarkers of neurodegeneration, as it is sensitive to subtle differences in the central nervous system from texture analysis of the neuroretina. Sex differences in the retina structure, as detected by layer thickness measurements from optical coherence tomography (OCT) data, have been discussed in the literature. However, the effect of sex on retinal interocular differences in healthy adults has been overlooked and remains largely unreported. Methods: We computed mean value fundus images for the neuroretina layers as imaged by OCT of healthy individuals. Texture metrics were obtained from these images to assess whether women and men have the same retina texture characteristics in both eyes. Texture features were tested for group mean differences between the right and left eye. Results: Corrected texture differences exist only in the female group. Conclusions: This work illustrates that the differences between the right and left eyes manifest differently in females and males. This further supports the need for tight control and minute analysis in studies where interocular asymmetry may be used as a disease biomarker, and the potential of texture analysis applied to OCT imaging to spot differences in the retina. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

► Show Figures

Figure 1

21 pages, 6929 KiB

Open AccessArticle

Arteriovenous Length Ratio: A Novel Method for Evaluating Retinal Vasculature Morphology and Its Diagnostic Potential in Eye-Related Diseases

by Sufian A. Badawi, Maen Takruri, Mohammad Al-Hattab, Ghaleb Aldoboni, Djamel Guessoum, Isam ElBadawi, Mohamed Aichouni, Imran Ali Chaudhry, Nasrullah Mahar and Ajay Kamath Nileshwar

J. Imaging 2023, 9(11), 253; https://doi.org/10.3390/jimaging9110253 - 20 Nov 2023

Viewed by 1603

Abstract

Retinal imaging is a non-invasive technique used to scan the back of the eye, enabling the extraction of potential biomarkers like the artery and vein ratio (AVR). This ratio is known for its association with various diseases, such as hypertensive retinopathy (HR) or diabetic retinopathy, and is crucial in assessing retinal health. HR refers to the morphological changes in retinal vessels caused by persistent high blood pressure. Timely identification of these alterations is crucial for preventing blindness and reducing the risk of stroke-related fatalities. The main objective of this paper is to propose a new method for assessing one of the morphological changes in the fundus through morphometric analysis of retinal images. The proposed method in this paper introduces a novel approach called the arteriovenous length ratio (AVLR), which has not been utilized in previous studies. Unlike commonly used measures such as the arteriovenous width ratio or tortuosity, AVLR focuses on assessing the relative length of arteries and veins in the retinal vasculature. The initial step involves segmenting the retinal blood vessels and distinguishing between arteries and veins; AVLR is calculated based on artery and vein caliber measurements for both eyes. Nine equations are used, and the length of both arteries and veins is measured in the region of interest (ROI) covering the optic disc for each eye. Using the AV-Classification dataset, the efficiency of the iterative AVLR assessment is evalutaed. The results show that the proposed approach performs better than the existing methods. By introducing AVLR as a diagnostic feature, this paper contributes to advancing retinal imaging analysis. It provides a valuable tool for the timely diagnosis of HR and other eye-related conditions and represents a novel diagnostic-feature-based method that can be integrated to serve as a clinical decision support system. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

► Show Figures

Figure 1

24 pages, 2648 KiB

Open AccessArticle

Retinal Microvasculature Image Analysis Using Optical Coherence Tomography Angiography in Patients with Post-COVID-19 Syndrome

by Maha Noor, Orlaith McGrath, Ines Drira and Tariq Aslam

J. Imaging 2023, 9(11), 234; https://doi.org/10.3390/jimaging9110234 - 24 Oct 2023

Cited by 1 | Viewed by 1959

Abstract

Several optical coherence tomography angiography (OCT-A) studies have demonstrated retinal microvascular changes in patients post-SARS-CoV-2 infection, reflecting retinal-systemic microvasculature homology. Post-COVID-19 syndrome (PCS) entails persistent symptoms following SARS-CoV-2 infection. In this study, we investigated the retinal microvasculature in PCS patients using OCT-angiography and analysed the macular retinal nerve fibre layer (RNFL) and ganglion cell layer (GCL) thickness via spectral domain-OCT (SD-OCT). Conducted at the Manchester Royal Eye Hospital, UK, this cross-sectional study compared 40 PCS participants with 40 healthy controls, who underwent ophthalmic assessments, SD-OCT, and OCT-A imaging. OCT-A images from the superficial capillary plexus (SCP) were analysed using an in-house specialised software, OCT-A vascular image analysis (OCTAVIA), measuring the mean large vessel and capillary intensity, vessel density, ischaemia areas, and foveal avascular zone (FAZ) area and circularity. RNFL and GCL thickness was measured using the OCT machine’s software. Retinal evaluations occurred at an average of 15.2 ± 6.9 months post SARS-CoV-2 infection in PCS participants. Our findings revealed no significant differences between the PCS and control groups in the OCT-A parameters or RNFL and GCL thicknesses, indicating that no long-term damage ensued in the vascular bed or retinal layers within our cohort, providing a degree of reassurance for PCS patients. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

► Show Figures

Figure 1

20 pages, 5567 KiB

Open AccessArticle

Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks

by Mohan Bhandari, Tej Bahadur Shahi and Arjun Neupane

J. Imaging 2023, 9(10), 219; https://doi.org/10.3390/jimaging9100219 - 11 Oct 2023

Cited by 1 | Viewed by 1768

Abstract

Optical Coherence Tomography (OCT) is an imperative symptomatic tool empowering the diagnosis of retinal diseases and anomalies. The manual decision towards those anomalies by specialists is the norm, but its labor-intensive nature calls for more proficient strategies. Consequently, the study recommends employing a Convolutional Neural Network (CNN) for the classification of OCT images derived from the OCT dataset into distinct categories, including Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The average k-fold (k = 10) training accuracy, test accuracy, validation accuracy, training loss, test loss, and validation loss values of the proposed model are 96.33%, 94.29%, 94.12%, 0.1073, 0.2002, and 0.1927, respectively. Fast Gradient Sign Method (FGSM) is employed to introduce non-random noise aligned with the cost function’s data gradient, with varying epsilon values scaling the noise, and the model correctly handles all noise levels below 0.1 epsilon. Explainable AI algorithms: Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are utilized to provide human interpretable explanations approximating the behaviour of the model within the region of a particular retinal image. Additionally, two supplementary datasets, namely, COVID-19 and Kidney Stone, are assimilated to enhance the model’s robustness and versatility, resulting in a level of precision comparable to state-of-the-art methodologies. Incorporating a lightweight CNN model with 983,716 parameters,

2.37 \times 10^{8}

floating point operations per second (FLOPs) and leveraging explainable AI strategies, this study contributes to efficient OCT-based diagnosis, underscores its potential in advancing medical diagnostics, and offers assistance in the Internet-of-Medical-Things. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

► Show Figures

Figure 1

18 pages, 5996 KiB

Open AccessArticle

Multi-Fundus Diseases Classification Using Retinal Optical Coherence Tomography Images with Swin Transformer V2

by Zhenwei Li, Yanqi Han and Xiaoli Yang

J. Imaging 2023, 9(10), 203; https://doi.org/10.3390/jimaging9100203 - 29 Sep 2023

Viewed by 1232

Abstract

Fundus diseases cause damage to any part of the retina. Untreated fundus diseases can lead to severe vision loss and even blindness. Analyzing optical coherence tomography (OCT) images using deep learning methods can provide early screening and diagnosis of fundus diseases. In this paper, a deep learning model based on Swin Transformer V2 was proposed to diagnose fundus diseases rapidly and accurately. In this method, calculating self-attention within local windows was used to reduce computational complexity and improve its classification efficiency. Meanwhile, the PolyLoss function was introduced to further improve the model’s accuracy, and heat maps were generated to visualize the predictions of the model. Two independent public datasets, OCT 2017 and OCT-C8, were applied to train the model and evaluate its performance, respectively. The results showed that the proposed model achieved an average accuracy of 99.9% on OCT 2017 and 99.5% on OCT-C8, performing well in the automatic classification of multi-fundus diseases using retinal OCT images. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

► Show Figures

Figure 1

29 pages, 3778 KiB

Open AccessArticle

Advancements in Cataract Detection: The Systematic Development of LeNet-Convolutional Neural Network Models

by Thittaporn Ganokratanaa, Mahasak Ketcham and Patiyuth Pramkeaw

J. Imaging 2023, 9(10), 197; https://doi.org/10.3390/jimaging9100197 - 26 Sep 2023

Cited by 1 | Viewed by 2186

Abstract

Regular screening and timely treatment play a crucial role in addressing the progression and visual impairment caused by cataracts, the leading cause of blindness in Thailand and many other countries. Despite the potential for prevention and successful treatment, patients often delay seeking medical attention due to the gradual and relatively asymptomatic nature of cataracts. To address this challenge, this research focuses on the identification of cataract abnormalities using image processing techniques and machine learning for preliminary assessment. The LeNet-convolutional neural network (LeNet-CNN) model is employed to train a dataset of digital camera images, and its performance is compared to the support vector machine (SVM) model in categorizing cataract abnormalities. The evaluation demonstrates that the LeNet-CNN model achieves impressive results in the testing phase. It attains an accuracy rate of 96%, exhibiting a sensitivity of 95% for detecting positive cases and a specificity of 96% for accurately identifying negative cases. These outcomes surpass the performance of previous studies in this field. This highlights the accuracy and effectiveness of the proposed approach, particularly the superior performance of LeNet-CNN. By utilizing image processing technology and convolutional neural networks, this research provides an effective tool for initial cataract screening. Patients can independently assess their eye health by capturing self-images, facilitating early intervention and medical consultations. The proposed method holds promise in enhancing the preliminary assessment of cataracts, enabling early detection and timely access to appropriate care. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

► Show Figures

Figure 1

Other

Jump to: Research

22 pages, 605 KiB

Open AccessSystematic Review

Denoising of Optical Coherence Tomography Images in Ophthalmology Using Deep Learning: A Systematic Review

by Hanya Ahmed, Qianni Zhang, Robert Donnan and Akram Alomainy

J. Imaging 2024, 10(4), 86; https://doi.org/10.3390/jimaging10040086 - 01 Apr 2024

Viewed by 748

Abstract

Imaging from optical coherence tomography (OCT) is widely used for detecting retinal diseases, localization of intra-retinal boundaries, etc. It is, however, degraded by speckle noise. Deep learning models can aid with denoising, allowing clinicians to clearly diagnose retinal diseases. Deep learning models can be considered as an end-to-end framework. We selected denoising studies that used deep learning models with retinal OCT imagery. Each study was quality-assessed through image quality metrics (including the peak signal-to-noise ratio—PSNR, contrast-to-noise ratio—CNR, and structural similarity index metric—SSIM). Meta-analysis could not be performed due to heterogeneity in the methods of the studies and measurements of their performance. Multiple databases (including Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. From the 95 potential studies identified, a total of 41 were evaluated thoroughly. Fifty-four of these studies were excluded after full text assessment depending on whether deep learning (DL) was utilized or the dataset and results were not effectively explained. Numerous types of OCT images are mentioned in this review consisting of public retinal image datasets utilized purposefully for denoising OCT images (n = 37) and the Optic Nerve Head (ONH) (n = 4). A wide range of image quality metrics was used; PSNR and SNR that ranged between 8 and 156 dB. The minority of studies (n = 8) showed a low risk of bias in all domains. Studies utilizing ONH images produced either a PSNR or SNR value varying from 8.1 to 25.7 dB, and that of public retinal datasets was 26.4 to 158.6 dB. Further analysis on denoising models was not possible due to discrepancies in reporting that did not allow useful pooling. An increasing number of studies have investigated denoising retinal OCT images using deep learning, with a range of architectures being implemented. The reported increase in image quality metrics seems promising, while study and reporting quality are currently low. Full article

(This article belongs to the Special Issue Advances in Retinal Image Processing)

► Show Figures

Journal Menu

Journal Browser

Advances in Retinal Image Processing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI