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J. Imaging
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Frontiers in Retinal Image Processing
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Frontiers in Retinal Image Processing

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 31329

Special Issue Editors

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
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

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.

Focusing on this direction, 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 disorders classification from retinal images
  • Early stage diagnosis and grading of retinal disorders
  • Hand-held or computerized devices for retinal image acquisition
  • Restoration and enhancement of retinal images
  • Analysis of retinal disorders using multi-modal 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

Prof. Dr. Vasudevan Lakshminarayanan
Dr. P. Jidesh
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

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Published Papers (6 papers)

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Editorial

Jump to: Research, Review

2 pages, 146 KiB  
Editorial
Frontiers in Retinal Image Processing
by Vasudevan Lakshminarayanan and P. Jidesh
J. Imaging 2022, 8(10), 265; https://doi.org/10.3390/jimaging8100265 - 29 Sep 2022
Viewed by 1224
Abstract
Visual impairment is considered as a primary global challenge in the present era [...] Full article
(This article belongs to the Special Issue Frontiers in Retinal Image Processing)

Research

Jump to: Editorial, Review

19 pages, 7727 KiB  
Article
Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets
by Sunil Kumar Yadav, Rahele Kafieh, Hanna Gwendolyn Zimmermann, Josef Kauer-Bonin, Kouros Nouri-Mahdavi, Vahid Mohammadzadeh, Lynn Shi, Ella Maria Kadas, Friedemann Paul, Seyedamirhosein Motamedi and Alexander Ulrich Brandt
J. Imaging 2022, 8(5), 139; https://doi.org/10.3390/jimaging8050139 - 17 May 2022
Cited by 7 | Viewed by 3737
Abstract
Reliable biomarkers quantifying neurodegeneration and neuroinflammation in central nervous system disorders such as Multiple Sclerosis, Alzheimer’s dementia or Parkinson’s disease are an unmet clinical need. Intraretinal layer thicknesses on macular optical coherence tomography (OCT) images are promising noninvasive biomarkers querying neuroretinal structures with [...] Read more.
Reliable biomarkers quantifying neurodegeneration and neuroinflammation in central nervous system disorders such as Multiple Sclerosis, Alzheimer’s dementia or Parkinson’s disease are an unmet clinical need. Intraretinal layer thicknesses on macular optical coherence tomography (OCT) images are promising noninvasive biomarkers querying neuroretinal structures with near cellular resolution. However, changes are typically subtle, while tissue gradients can be weak, making intraretinal segmentation a challenging task. A robust and efficient method that requires no or minimal manual correction is an unmet need to foster reliable and reproducible research as well as clinical application. Here, we propose and validate a cascaded two-stage network for intraretinal layer segmentation, with both networks being compressed versions of U-Net (CCU-INSEG). The first network is responsible for retinal tissue segmentation from OCT B-scans. The second network segments eight intraretinal layers with high fidelity. At the post-processing stage, we introduce Laplacian-based outlier detection with layer surface hole filling by adaptive non-linear interpolation. Additionally, we propose a weighted version of focal loss to minimize the foreground–background pixel imbalance in the training data. We train our method using 17,458 B-scans from patients with autoimmune optic neuropathies, i.e., multiple sclerosis, and healthy controls. Voxel-wise comparison against manual segmentation produces a mean absolute error of 2.3 μm, outperforming current state-of-the-art methods on the same data set. Voxel-wise comparison against external glaucoma data leads to a mean absolute error of 2.6 μm when using the same gold standard segmentation approach, and 3.7 μm mean absolute error in an externally segmented data set. In scans from patients with severe optic atrophy, 3.5% of B-scan segmentation results were rejected by an experienced grader, whereas this was the case in 41.4% of B-scans segmented with a graph-based reference method. The validation results suggest that the proposed method can robustly segment macular scans from eyes with even severe neuroretinal changes. Full article
(This article belongs to the Special Issue Frontiers in Retinal Image Processing)
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16 pages, 5567 KiB  
Article
Unsupervised Approaches for the Segmentation of Dry ARMD Lesions in Eye Fundus cSLO Images
by Clément Royer, Jérémie Sublime, Florence Rossant and Michel Paques
J. Imaging 2021, 7(8), 143; https://doi.org/10.3390/jimaging7080143 - 11 Aug 2021
Cited by 6 | Viewed by 2792
Abstract
Age-related macular degeneration (ARMD), a major cause of sight impairment for elderly people, is still not well understood despite intensive research. Measuring the size of the lesions in the fundus is the main biomarker of the severity of the disease and as such [...] Read more.
Age-related macular degeneration (ARMD), a major cause of sight impairment for elderly people, is still not well understood despite intensive research. Measuring the size of the lesions in the fundus is the main biomarker of the severity of the disease and as such is widely used in clinical trials yet only relies on manual segmentation. Artificial intelligence, in particular automatic image analysis based on neural networks, has a major role to play in better understanding the disease, by analyzing the intrinsic optical properties of dry ARMD lesions from patient images. In this paper, we propose a comparison of automatic segmentation methods (classical computer vision method, machine learning method and deep learning method) in an unsupervised context applied on cSLO IR images. Among the methods compared, we propose an adaptation of a fully convolutional network, called W-net, as an efficient method for the segmentation of ARMD lesions. Unlike supervised segmentation methods, our algorithm does not require annotated data which are very difficult to obtain in this application. Our method was tested on a dataset of 328 images and has shown to reach higher quality results than other compared unsupervised methods with a F1 score of 0.87, while having a more stable model, even though in some specific cases, texture/edges-based methods can produce relevant results. Full article
(This article belongs to the Special Issue Frontiers in Retinal Image Processing)
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17 pages, 1781 KiB  
Article
EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection
by Venkatesh Krishna Adithya, Bryan M. Williams, Silvester Czanner, Srinivasan Kavitha, David S. Friedman, Colin E. Willoughby, Rengaraj Venkatesh and Gabriela Czanner
J. Imaging 2021, 7(6), 92; https://doi.org/10.3390/jimaging7060092 - 30 May 2021
Cited by 12 | Viewed by 3948
Abstract
Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In [...] Read more.
Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed “SpaGen” We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, “EffUnet-SpaGen”, is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings. Full article
(This article belongs to the Special Issue Frontiers in Retinal Image Processing)
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Review

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19 pages, 879 KiB  
Review
Towards a Connected Mobile Cataract Screening System: A Future Approach
by Wan Mimi Diyana Wan Zaki, Haliza Abdul Mutalib, Laily Azyan Ramlan, Aini Hussain and Aouache Mustapha
J. Imaging 2022, 8(2), 41; https://doi.org/10.3390/jimaging8020041 - 10 Feb 2022
Cited by 13 | Viewed by 7647
Abstract
Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant [...] Read more.
Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities. Full article
(This article belongs to the Special Issue Frontiers in Retinal Image Processing)
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26 pages, 19959 KiB  
Review
Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey
by Vasudevan Lakshminarayanan, Hoda Kheradfallah, Arya Sarkar and Janarthanam Jothi Balaji
J. Imaging 2021, 7(9), 165; https://doi.org/10.3390/jimaging7090165 - 27 Aug 2021
Cited by 72 | Viewed by 10089
Abstract
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this [...] Read more.
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented. Full article
(This article belongs to the Special Issue Frontiers in Retinal Image Processing)
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