Optic Disc Localization Research Articles

Multiresolution cascaded attention U-Net for localization and segmentation of optic disc and fovea in fundus images

Identification of retinal diseases in automated screening methods, such as those used in clinical settings or computer-aided diagnosis, usually depends on the localization and segmentation of the Optic Disc (OD) and fovea. However, this task is difficult since these anatomical features have irregular spatial, texture, and shape characteristics, limited sample sizes, and domain shifts due to different data distributions across datasets. This study proposes a novel Multiresolution Cascaded Attention U-Net (MCAU-Net) model that addresses these problems by optimally balancing receptive field size and computational efficiency. The MCAU-Net utilizes two skip connections to accurately localize and segment the OD and fovea in fundus images. We incorporated a Multiresolution Wavelet Pooling Module (MWPM) into the CNN at each stage of U-Net input to compensate for spatial information loss. Additionally, we integrated a cascaded connection of the spatial and channel attentions as a skip connection in MCAU-Net to concentrate precisely on the target object and improve model convergence for segmenting and localizing OD and fovea centers. The proposed model has a low parameter count of 0.8 million, improving computational efficiency and reducing the risk of overfitting. For OD segmentation, the MCAU-Net achieves high IoU values of 0.9771, 0.945, and 0.946 for the DRISHTI-GS, DRIONS-DB, and IDRiD datasets, respectively, outperforming previous results for all three datasets. For the IDRiD dataset, the MCAU-Net locates the OD center with an Euclidean Distance (ED) of 16.90 pixels and the fovea center with an ED of 33.45 pixels, demonstrating its effectiveness in overcoming the common limitations of state-of-the-art methods.

Research trends and hotspots in fundus image segmentation from 2007 to 2023: A bibliometric analysis

ObjectiveTo understand the current status, research hotspots, and trends of automatic segmentation of fundus lesion images worldwide, providing a reference for subsequent related studies. MethodsThe electronic database Web of Science Core Collection was searched for research in the field of automatic segmentation of fundus lesion images from 2007 to 2023. Visualization maps of countries, authors, institutions, journals, references, and keywords were generated and analyzed using the CiteSpace and VOSviewer software. ResultsAfter deduplication, 707 publications were sorted out, showing an overall increasing trend in publication volume. The countries with the highest publication counts were China, followed by India, the USA, the UK, Spain, Pakistan, and Singapore. A high degree of collaboration was observed among authors, and they cooperated widely. The keywords included "diabetic retinopathy," "deep learning," "vessel segmentation," "retinal images," "optic disc localization," and so forth, with keyword bursts starting in 2018 for "retinal images," "machine learning," "biomedical imaging," "deep learning," "convolutional neural networks," and "transfer learning." The most prolific author was U Rajendra Acharya from the University of Southern Queensland, and the journal with the most publications was Computer Methods and Programs in Biomedicine. ConclusionsCompared with manual segmentation of fundus lesion images, the use of deep learning models for segmentation is more efficient and accurate, which is crucial for patients with eye diseases. Although the number of related publications globally is relatively small, a growing trend is still witnessed, with broad connections between countries and authors, mainly concentrated in East Asia and Europe. Research institutions in this field are limited, and hence, the research on diabetic retinopathy and retinal vessel segmentation should be strengthened to promote the development of this area.

A Comprehensive Approach for an Interpretable Diabetic Macular Edema Grading System Based on ConvUNext

Diabetic macular edema (DME) is a leading cause of vision impairment in diabetic patients, necessitating a timely and accurate diagnosis. This paper proposes a comprehensive system for DME grading using retinal fundus images. Our approach integrates multiple deep learning modules, each designed to address key aspects of the diagnostic process. The first module employs the ConvUNeXt model for segmenting hard exudates (HaEx), crucial indicators of DME. The second module uses RetinaNet for precise optic disc (OD) localization, which is essential for subsequent macula localization. The third module refines macula localization, leveraging preprocessing techniques to enhance image clarity. Finally, our system consolidates these results to provide interpretable DME grading. Experimental evaluations were conducted on the Messidor dataset, demonstrating the system’s robust performance. The HaEx segmentation module achieved a mean IoU of 70.5% and a Dice coefficient of 0.64. The OD localization module showed perfect accuracy, recall, and precision at 1.0. For macula localization, our method satisfied the 1R criterion with 99.38% accuracy. The DME grading module achieved an overall accuracy of 91.12%, with an AUC of 0.9334. Our method offers a balanced performance across accuracy, sensitivity, and specificity compared to other non-interpretable and partially interpretable methods.

Two stage-network: Automatic localization of Optic Disc (OD) and classification of glaucoma in fundus images using deep learning techniques

Two stage-network: Automatic localization of Optic Disc (OD) and classification of glaucoma in fundus images using deep learning techniques

Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17-19 Years) Candidates to Airforce Cadets.

Artificial intelligence (AI)-assisted ultra-widefield (UWF) fundus photographic interpretation is beneficial to improve the screening of fundus abnormalities. Therefore we constructed an AI machine-learning approach and performed preliminary training and validation. We proposed a two-stage deep learning-based framework to detect early retinal peripheral degeneration using UWF images from the Chinese Air Force cadets' medical selection between February 2016 and June 2022. We developed a detection model for the localization of optic disc and macula, which are used to find the peripheral areas. Then we developed six classification models for the screening of various retinal cases. We also compared our proposed framework with two baseline models reported in the literature. The performance of the screening models was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval. A total of 3911 UWF fundus images were used to develop the deep learning model. The external validation included 760 UWF fundus images. The results of comparison study revealed that our proposed framework achieved competitive performance compared to existing baselines while also demonstrating significantly faster inference time. The developed classification models achieved an average AUC of 0.879 on six different retinal cases in the external validation dataset. Our two-stage deep learning-based framework improved the machine learning efficiency of the AI model for fundus images with high resolution and many interference factors by maximizing the retention of valid information and compressing the image file size. This machine learning model may become a new paradigm for developing UWF fundus photography AI-assisted diagnosis.

Automatic zoning for retinopathy of prematurity with a key area location system.

Retinopathy of prematurity (ROP) usually occurs in premature or low birth weight infants and has been an important cause of childhood blindness worldwide. Diagnosis and treatment of ROP are mainly based on stage, zone and disease, where the zone is more important than the stage for serious ROP. However, due to the great subjectivity and difference of ophthalmologists in the diagnosis of ROP zoning, it is challenging to achieve accurate and objective ROP zoning diagnosis. To address it, we propose a new key area location (KAL) system to achieve automatic and objective ROP zoning based on its definition, which consists of a key point location network and an object detection network. Firstly, to achieve the balance between real-time and high-accuracy, a lightweight residual heatmap network (LRH-Net) is designed to achieve the location of the optic disc (OD) and macular center, which transforms the location problem into a pixel-level regression problem based on the heatmap regression method and maximum likelihood estimation theory. In addition, to meet the needs of clinical accuracy and real-time detection, we use the one-stage object detection framework Yolov3 to achieve ROP lesion location. Finally, the experimental results have demonstrated that the proposed KAL system has achieved better performance on key point location (6.13 and 17.03 pixels error for OD and macular center location) and ROP lesion location (93.05% for AP50), and the ROP zoning results based on it have good consistency with the results manually labeled by clinicians, which can support clinical decision-making and help ophthalmologists correctly interpret ROP zoning, reducing subjective differences of diagnosis and increasing the interpretability of zoning results.

Automated optic disk segmentation for optic disk edema classification using factorized gradient vector flow

One significant ocular symptom of neuro-ophthalmic disorders of the optic disk (OD) is optic disk edema (ODE). The etiologies of ODE are broad, with various symptoms and effects. Early detection of ODE can prevent potential vision loss and fatal vision problems. The texture of edematous OD significantly differs from the non-edematous OD in retinal images. As a result, techniques that usually work for non-edematous cases may not work well for edematous cases. We propose a fully automatic OD classification of edematous and non-edematous OD on fundus image collections containing a mixture of edematous and non-edematous ODs. The proposed algorithm involved localization, segmentation, and classification of edematous and non-edematous OD. The factorized gradient vector flow (FGVF) was used to segment the ODs. The OD type was classified using a linear support vector machine (SVM) based on 27 features extracted from the vessels, GLCM, color, and intensity line profile. The proposed method was tested on 295 images with 146 edematous cases and 149 non-edematous cases from three datasets. The segmentation achieves an average precision of 88.41%, recall of 89.35%, and F1-Score of 86.53%. The average classification accuracy is 99.40% and outperforms the state-of-the-art method by 3.43%.

Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting

Aims/Purpose: To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non‐experts.Methods: The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths.Results: QUARTZ's performance was evaluated on a subset of 3682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of −0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets.Conclusions: QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources.

An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

OD-DeepNet: Semantic Classification by Deep Learning for Optic Disc Localization

OD-DeepNet: Semantic Classification by Deep Learning for Optic Disc Localization

Evaluation of choroidal thickness and retinal nerve fiber layer thickness in Chinese pregnant women and healthy non-pregnant women

PurposeTo evaluate choroidal thickness and retinal nerve fiber layer (RNFL) thickness in different trimesters using enhanced depth imaging optical coherence tomography (EDI-OCT). MethodsA prospective comparative study included 45 healthy pregnant women in the first trimester, 45 women in the second, 45 women in the third and 45 healthy non-pregnant women as the control group. Macular choroidal thickness was measured at three locations: The subfoveal, 1 ​mm temporal, and 1 ​mm nasal from the fovea with EDI-OCT. Peripapillary choroidal thickness (PPCT) and RNFL thickness parameters were automatically calculated by the Spectralis OCT. ResultsThe subfoveal, temporal and nasal macular choroidal thickness were all significantly thicker in the second trimester, compared with those parameters in the first, the third trimesters and the control group (all P ​< ​0.05). The PPCT was significantly increased in the second trimeter compared with the control group at global, temporal, temporal inferior, nasal and nasal inferior positions (all P ​< ​0.05). The RNFL thickness was also significantly increased in pregnant women at nasal superior and nasal inferior quadrants (all P ​< ​0.05). ConclusionsThe choroidal thickness in pregnant women was found to be thicker than the control group, regardless of macular or optic disc location. Findings of RNFL thickening might indicate subclinical involvement of the central nervous system.

A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features

One of the major causes of blindness in human beings is the diabetic retinopathy (DR). To prevent blindness, early detection of DR is therefore necessary. In this paper, a hybrid model is proposed for diagnosing DR from fundus images. A combination of morphological image processing and Inception v3 deep learning techniques are exploited to detect DR as well as to classify healthy, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). The proposed algorithm was carried out in several steps such as segmentation of blood vessels, localization and removal of optic disc, and macula, abnormal features detection (microaneurysms, hemorrhages, and neovascularization), and classification. Microaneurysms and hemorrhages that appear in the retina are the early signs of DR. In this work, we have detected microaneurysms and hemorrhages by applying dynamic contrast limited adaptive histogram equalization and threshold value on overlapping patched images. An overall accuracy of 96.83% is obtained to classify DR into five different stages. The better performance demonstrates the effectiveness and novelty of the proposed work as compared to the recent reported work.

Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting.

To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non-experts. The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths. QUARTZ's performance was evaluated on a subset of 3,682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of -0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets. QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources.

The Methods of Determining Temporal Direction Based on Asymmetric Information of the Optic Disc for Optimal Fovea Detection

Accurate localization of the fovea in fundus images is essential for diagnosing retinal diseases. Existing methods often require extensive data and complex processes to achieve high accuracy, posing challenges for practical implementation. In this paper, we propose an effective and efficient approach for fovea detection using simple image processing operations and a geometric approach based on the optic disc’s position. A key contribution of this study is the successful determination of the temporal direction by leveraging readable asymmetries related to the optic disc and its surroundings. We discuss three methods based on asymmetry conditions, including blood vessel distribution, cup disc inclination, and optic disc location ratio, for detecting the temporal direction. This enables precise determination of the optimal foveal region of interest. Through this optimized fovea region, fovea detection is achieved using straightforward morphological and image processing operations. Extensive testing on popular datasets (DRIVE, DiaretDB1, and Messidor) demonstrates outstanding accuracy of 99.04% and a rapid execution time of 0.251 s per image. The utilization of asymmetrical conditions for temporal direction detection provides a significant advantage, offering high accuracy and efficiency while competing with existing methods.

Proliferative Diabetic Retinopathy Diagnosis Using Varying-Scales Filter Banks and Double-Layered Thresholding

Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage. It causes retinal detachment, which may lead to complete blindness in severe cases. In this paper, a novel method is proposed for the detection and grading of neovascularization. The proposed system first performs pre-processing on input retinal images to enhance the vascular pattern, followed by blood vessel segmentation and optic disc localization. Then various features are tested on the candidate regions with different thresholds. In this way, positive and negative advanced diabetic retinopathy cases are separated. Optic disc coordinates are applied for the grading of neovascularization as NVD or NVE. The proposed algorithm improves the quality of automated diagnostic systems by eliminating normal blood vessels and exudates that might cause hindrances in accurate disease detection, thus resulting in more accurate detection of abnormal blood vessels. The evaluation of the proposed system has been carried out using performance parameters such as sensitivity, specificity, accuracy, and positive predictive value (PPV) on a publicly available standard retinal image database and one of the locally available databases. The proposed algorithm gives an accuracy of 98.5% and PPV of 99.8% on MESSIDOR and an accuracy of 96.5% and PPV of 100% on the local database.

Optic Disc Localization in Normal and Pathological Retinal Images Using Dictionary-Based Approach

Optic Disc Localization in Normal and Pathological Retinal Images Using Dictionary-Based Approach

Optic Disc Localization using Fuzzy C Mean and DB Scan Clustering - A Comparative Analysis

In the diagnosis and early detection of Glaucoma and diabetic retinopathy, when delicate vasculature grows in the retina, precise identification and localization of the border optic disc are highly significant. This research provides an automated method to localize and detect the optic disc. The proposed method uses the clustering methodology to locate the optic disc region. Fuzzy C Mean and Density Based Scan (DB Scan) clustering approach is evaluated on the publicly accessible DRIVE, diaretdb1, diaretdb0, and databases, which were created to aid comparative investigations on optic disc localization and detection in retinal images. With Diaretdb0 and Drive DB, the DB Scan clustering approach obtained an accuracy of 94.11% and 81.18%, respectively, which is better than the Fuzzy C mean, and it performs DB scan better for the DiaretDB1 dataset.

Exudate identification in retinal fundus images using precise textural verifications

One of the most salient diseases of retina is Diabetic Retinopathy (DR) which may lead to irreparable damages to eye vision in the advanced phases. A large number of the people infected with diabetes experience DR. The early identification of DR signs facilitates the treatment process and prevents from blindness. Hard Exudates (HE) are bright lesions appeared in retinal fundus images of DR patients. Thus, the detection of HEs is an important task preventing the progress of DR. However, the detection of HEs is a challenging process due to their different appearance features. In this paper, an automatic method for the identification of HEs with various sizes and shapes is proposed. The method works based on a pixel-wise approach. It considers several semi-circular regions around each pixel. For each semi-circular region, the intensity changes around several directions and non-necessarily equal radiuses are computed. All pixels for which several semi-circular regions include considerable intensity changes are considered as the pixels located in HEs. In order to reduce false positives, an optic disc localization method is proposed in the post-processing phase. The performance of the proposed method has been evaluated on DIARETDB0 and DIARETDB1 datasets. The experimental results confirm the improved performance of the suggested method in term of accuracy.

Optic disc localization using graph traversal algorithm along blood vessel in polar retinal image

The optic disc (OD) is an important landmark of the retina and its location is essential for computer-aided diagnosis of diabetic retinopathy and glaucoma. This paper presented a new method that applies the proposed graph traversal algorithm for vertical vascular infrastructure to detect root nodes representing the location of optical discs. The proposed algorithms were designed to process the terrific view of blood vessels from a polar image without depending on the prominent OD characteristic of circular/elliptical shape and brightness. Before constructing the vascular infrastructure, the new multi-scale and multi-orientation top-hat filters were proposed to properly approximate blood vessels of different sizes and directions. The proposed method provided efficiency and certainty for complex retinal images such as low contrast OD with low illumination and distorted OD shape due to complex vascular structure. The algorithms evaluation was performed on five public fundus image databases for retinal image analysis. Experimental results revealed that the proposed algorithm identified valid optical discs with high accuracy as 91.21%, 95.09%, 88.45%, 91.66%, and 95.01% for the five corresponding data sets and outperformed comparison methods.

CFLHCF: Simultaneous Detection of the Optic Disc and Exudates Using Color Features, Local Homogeneity and Contextual Features

Detecting Optic Disc (OD) and Exudates (EXs) in the fundus images has been challenging and demanding for the computer-aided diagnosis system. Existing algorithms for detecting OD and EXs are mainly based on traditional learning methods that heavily rely on enhanced OD and EXs features. Unlike traditional learning methods, a novel simultaneous detection of OD and EXs is presented. In this proposed novel Color Features, Local Homogeneity and Contextual Features (CFLHCF), the input original fundus image is preprocessed by color normalization, contrast enhancement, noise removal, and OD localization which the EXs is differentiated from its background information. Then, the preprocessed images are given as the input to Mathematical Morphology Binary Segmentation (MMBS) with Sobel Edge Detection (SED) technique, which detects the EXs from the given fundus images. An MMBS with a SED technique is implemented to boost a highly accurate segmented model for small EXs regions of EXs. The DiaretDB0, DiaretDB1, and STARE datasets are used to validate the proposed method. On the DiaretDB0 dataset, this technique archived an average sensitivity of 98.44% for EXs and a specificity of 98.72% for non‒EXs, which can potentially classify EXs even when the regions are trivial. With respect to sensitivity and specificity values, this method outperformed the previous state‒of‒the‒art methods by roughly 1.24% and 1.09% in the detection of EXs. Additionally, we show the EXs‒diagnosis in ~7 seconds per image.