A novel vessel segmentation algorithm for pathological en-face images based on matched filter

High-Definition and 3-dimensional Imaging of Macular Pathologies with High-speed Ultrahigh-Resolution Optical Coherence Tomography

Speckle noise generated during optical coherence tomography (OCT) imaging severely images image quality. In order to study the processing method of speckle noise reduction, this paper takes the OCT fundus image as the research object, and uses the median filtering, wavelet threshold method, Wiener filtering and anisotropic diffusion method to denoise the OCT fundus image. Finally, the effects of image noise reduction are objectively compared by four factors: signal-to-noise ratio (SNR), speckle index (Speckle-index), equivalent visual number (ENL) and edge retention coefficient (EPI). The results show that Wiener filtering and anisotropic diffusion method have better noise reduction effects.

ObjectiveRetinal parasitism by worms is a rare clinical occurrence and may cause diagnostic and therapeutic challenges. We present a girl with unilateral involvement who was first diagnosed at the age of 10. Change in parasite appearance inside the lesion was recorded by optical coherence tomography (OCT) 14 years apart.MethodsCase report.ResultsA 10-year-old girl was referred to our clinic with a presumptive diagnosis of a unilateral posterior pole mass. She had been examined at another institution for the left exotropia and decreased vision, without any accompanying systemic symptoms. Upon our examination, a subretinal lesion, of two disc diameters in size, was detected two disc diameters above the left optic disc, and a spiral-shaped, non-motile roundworm could be spotted inside the lesion. Adjacent chorioretinal atrophy and marked fibrosis surrounded the lesion, without any active inflammation. OCT sections through the lesion delineated the hyperreflective appearance of the spiraling roundworm. Fourteen years later, the worm inside the lesion had lost its spiraling form, and the remnants appeared as a coalesced whitish material inside the lesion on the OCT.DiscussionAlthough rare, retinal parasitism by worms presents significant diagnostic challenges due to its atypical presentation and the potential for misdiagnosis. In this case, the initial presentation of a subretinal lesion containing a spiral-shaped roundworm was notable for its lack of associated systemic symptoms and the absence of active inflammation, often seen in more common ocular infections. The long-term follow-up, using OCT, provided valuable insights into the infection’s natural course, showing the gradual degeneration and transformation of the parasite into a coalesced whitish material, over 14 years. This case underlines the importance of longitudinal imaging in understanding the progression of such unusual retinal conditions and the need for awareness of parasitic infections as a differential diagnosis in similar clinical scenarios.ConclusionsThe present case demonstrates the natural evolution of the inactive subretinal worm by OCT and color fundus images.

Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.

The problem of segmenting retinal blood vessels in fundus images arises from the challenges of accurately detecting and delineating vessels due to their complex structures, varying sizes, and overlapping features. Manual segmentation is time-consuming and prone to human error, leading to inconsistent results. Additionally, existing automated methods often struggle with low-quality images or variations in illumination, hindering their effectiveness. Therefore, there is a pressing need for an efficient and accurate automated system to improve segmentation outcomes for better diagnosis of retinal diseases. This study proposes a fully automated model for blood vessel segmentation in retinal fundus images, addressing key challenges such as poor image quality, weak vessel detection, and inhomogeneity in contrast. Macular degeneration and diabetic retinopathy are major causes of vision impairment, making accurate retinal analysis crucial. The proposed model enhances image quality through a novel pre-processing pipeline that includes logarithmic contrast enhancement, noise reduction using an improved complex wavelet transform with shrinkage, and anisotropic diffusion filtering for edge enhancement. The segmentation method combines morphological operations with an optimized Canny edge detector, effectively identifying and segmenting blood vessels. This approach aims to improve the accuracy and efficiency of retinal image analysis, overcoming the limitations of manual segmentation and complex vascular structures. The results obtained from the DRIVE dataset achieved high values for accuracy (Acc, 99%), sensitivity (Sen, 95.83%), specificity (Spe, 98.62%), positive predictive value (PPV, 91.34%), and negative predictive value (NPV, 94%). In addition, the results obtained using the high-resolution fundus dataset were equally satisfactory, achieving an Acc., Sen., Spe., PPV, and NPV of 99.11, 97.97, 98.97, 97.98, and 100%, respectively. These results outperform the gold standard and state-of-the-art schemes to date. The proposed approach increases the performance and reliability of the process of vessel detection in fundus images.

An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images

We previously developed a technique to acquire a SLO (scanning laser ophthalmoscope) like fundus intensity image from the raw spectra measured with spectral-domain optical coherence tomography (OCT), the same spectra used to generate a 3D OCT data set. This technique offers simultaneous fundus and OCT images and, therefore, solves the problem of registering a cross sectional OCT image to fundus features. However, the registration of high density OCT images is still an unsolved problem because no useful fundus image can be generated from the high density scans. High density OCT images can significantly improve the image quality and enhance the visualization of retinal structure, especially the structure of small lesions. We have developed a feature-based algorithm, which can register a high density OCT image on the fundus image generated from normal density scans. The algorithm was successfully tested for both normal and diseased eyes.

In 2010, Dr. Lawrence A. Yannuzzi published The Retinal Atlas, the magnum opus pictorial review of retinal disease. Multiple copies populate every library serving ophthalmology and optometry alike, and The Retinal Atlas remains the go-to source for clinical diagnosis of retinal disease. Dr. Yannuzzi’s name is synonymous with his standard-of-care masterwork, which undoubtedly led to accurate diagnosis, management, and treatment of inestimable cases of vision-threatening disease. In 2017, Drs. Freund, Sarraf, and Mieler, their mentor Dr. Yannuzzi, and numerous additional contributors published the second edition of The Retinal Atlas in response to proliferative advances in medical and surgical treatment of retinal disease coupled with substantive improvements in diagnostic imaging. The revised text successfully achieves its goal of providing a timely, consummate atlas of retinal disease, which, like the first edition, will ensure precise diagnoses, timely treatment, and retention of vision. The second edition astutely includes Dr. Yannuzzi’s preface to the first edition, which provides an invaluable historical and contemporary review of imaging techniques. A one-page color-coded guide on How to Use This Book was added depicting the myriad of imaging techniques used throughout the text. In addition to retinal histology, normal and red-free photography, fundus auto-fluorescence, and fluorescein angiography and indocyanine green angiography, the second edition has widely expanded use of spectral domain optical coherence tomography and adds optical coherence tomography angiography, near infrared reflectance, multicolored imaging, and ultra-wide-field imaging of photography, fluorescein angiography, fundus auto-fluorescence, and indocyanine green angiography. Like its predecessor, the second edition includes 15 chapters covering a plethora of conditions spanning congenital, hereditary, vascular, infectious, and degenerative disease. Many of the chapters now begin with topics divided into subdivisions based on structure and function, such as the widely expanded chapter 15 on congenital and developmental optic nerve anomalies. A more comprehensive section on retinitis pigmentosa includes macular effects, whereas superb fundus auto-fluorescence and multifocal electroretinography highlight the timely section on Plaquenil toxicity. Numerical grading scales were added for retinoblastoma, and sections on vitreous and macular degeneration are vastly expanded with remarkable optical coherence tomography, imagery. Multiple additions of optical coherence tomography, fundus auto-fluorescence, and ultra-wide-field imagery elevate The Retinal Atlas to unparalleled status. Cancer and melanoma-associated retinopathy apparently were not included, a minor suggestion for the next edition. The Retinal Atlas, second edition, is the principal source for accurate diagnosis of retinal disease. No other text combines histology, photography, and advanced imaging in such an illustrative manner with concise explanations. It is an essential text for eye care professionals ensuring timely diagnosis of retinal disease. Purchase of the text includes a readily accessible eBook version for computer, tablet, or smartphone, making Dr. Yannuzzi’s work of genius directly available in clinic. Jeff C. Rabin, OD, PhD, FAAOSan Antonio, TX

Artificial intelligence (AI) algorithms for the detection of retinoblastoma (RB) by fundus image analysis have been proposed as a potentially effective technique to facilitate diagnosis and screening programs. However, doubts remain about the accuracy of the technique, the best type of AI for this situation, and its feasibility for everyday use. Therefore, we performed a systematic review and meta-analysis to evaluate this issue. Following PRISMA 2020 guidelines, a comprehensive search of MEDLINE, Embase,ClinicalTrials.gov and IEEEX databases identified494studies whose titles and abstracts were screened for eligibility. We included diagnostic studies that evaluated the accuracy of AI in identifying retinoblastoma based on fundus imaging. Univariate and bivariate analysis was performed using the random effects model. The study protocol was registered in PROSPERO under CRD42024499221. Six studies with 9902 fundus images were included, of which 5944 (60%) had confirmed RB. Only one dataset used a semi-supervised machine learning (ML) based method, all other studies used supervised ML, three using architectures requiring high computational power and two using more economical models. The pooled analysis of all models showed a sensitivity of 98.2% (95% CI: 0.947-0.994), a specificity of 98.5% (95% CI: 0.916-0.998) and an AUC of 0.986 (95% CI: 0.970-0.989). Subgroup analyses comparing models with high and low computational power showed no significant difference (p=0.824). AI methods showed a high precision in the diagnosis of RB based on fundus images with no significant difference when comparing high and low computational power models, suggesting a viability of their use. Validation and cost-effectiveness studies are needed in different income countries. Subpopulations should also be analyzed, as AI may be useful as an initial screening tool in populations at high risk for RB, serving as a bridge to the pediatric ophthalmologist or ocular oncologist, who are scarce globally. What isknown Retinoblastoma is the most common intraocular cancer in childhood and diagnostic delay is the main factor leading to a poor prognosis. The application of machine learning techniques proposes reliable methods for screening and diagnosis of retinal diseases. What isnew The meta-analysis of the diagnostic accuracy of artificial intelligence methods for diagnosing retinoblastoma based on fundus images showed a sensitivity of 98.2% (95% CI: 0.947-0.994) and a specificity of 98.5% (95% CI: 0.916-0.998). There was no statistically significant difference in the diagnostic accuracy of high and low computational power models. The overall performance of supervised machine learning was best than unsupervised, although few studies were available on the second type.

This study presents an innovative Ensemble Disease Learning Algorithm (EDL) for the detection and classification of retinal diseases using fundus images. We enhance our method by incorporating deep learning techniques and multi-modal imaging data, including optical coherence tomography (OCT) images alongside fundus photographs, to provide a more comprehensive understanding of retinal pathology. The advanced EDL integrates Convolutional Neural Networks (CNNs) and attention mechanisms with Capsule Networks (CapsNet) and Support Vector Machine (SVM) classifiers for more nuanced feature extraction and classification. We introduce a novel ensemble adaptive weighting approach that dynamically adjusts classifier weights based on performance across disease types and severity levels, significantly improving the algorithm's handling of complex and rare cases. To enhance model interpretability, we implement an explainable AI component that provides visual heatmaps of the most significant regions for each diagnosis to clinicians. We evaluate the enhanced EDL on a large, diverse dataset encompassing multiple retinal diseases, including diabetic retinopathy, age-related macular degeneration, and glaucoma, across various ethnicities and age groups. Our results demonstrate superior accuracy, sensitivity, and specificity compared to our previous model and other state-of-the-art approaches. A prospective clinical validation study assesses the algorithm's real-world performance. This research advances automated retinal disease diagnosis by making it more robust, accurate, and clinically relevant, potentially improving patient outcomes and global eye care through early disease detection and treatment planning.

A hybrid segmentation algorithm is proposed is this paper to extract the blood vessels from the fundus image of retina. Fundus camera captures the posterior surface of the eye and the captured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinal haemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysis of vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the field of ophthalmology. It is derived from the literature review that no unique segmentation algorithm is suitable for images of different eye-related diseases and the degradation of the vessels differ from patient to patient. If the blood vessels are extracted from the fundus images, it will make the diagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithm exclusively for the extraction of blood vessels from the fundus image. The proposed algorithm is hybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement (MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, the area-based morphological operator is applied to highlight the blood vessels. To validate the proposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus (HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments the blood vessels with more accuracy than the existing algorithms.

The aim of this research is to develop a new automated blood vessel (BV) detection algorithm for optical coherence tomography (OCT) scans and corresponding fundus images. The algorithm provides a robust method to detect BV shadows (BVSs) using Radon transformation and other supporting image processing methods. The position of the BVSs is determined in OCT scans and the BV thickness is measured in the fundus images. Additionally, the correlation between BVS thickness and retinal nerve fiber layer (RNFL) thickness is determined. This correlation is of great interest since glaucoma, for example, can be identified by a loss of RNFL thickness.

Over the past years, a significant progress in genetic, functional and imaging diagnostics in hereditary retinal diseases has been made. Optical coherence tomography (OCT) as well as fundus autofluorescence (FAF) allow for high-resolution, non-invasive imaging - from various perspectives - of retinal and choroidal layers of the posterior fundus. Both techniques have gained more and more significance in the diagnosis of hereditary retinal diseases. Of all patients presented in this review, extensive family history was taken and a clinical ophthalmological examination performed. OCT scans as well as FAF images were acquired and compared to results of other functional and molecular genetic tests in the context of each disease. The presented cases in this review addressing hereditary retinal diseases (Best's disease, Stargardt's disease, cone-rod dystrophy, retinitis pigmentosa, achromatopsia, and X-linked retinoschisis) show the significance of ophthalmic imaging (OCT + FAF) for a targeted diagnosis of hereditary retinal diseases. The described imaging techniques (OCT + FAF) are becoming more and more important in the diagnosis of hereditary retinal diseases. Due to increasing availability of the devices, earlier detection of typical morphological changes not seen in clinical fundoscopy is feasible.

Immiscible two-phase flow in porous media with mixed wet conditions was examined using a capillary fiber bundle model, which is analytically solvable, and a dynamic pore network model. The mixed wettability was implemented in the models by allowing each tube or link to have a different wetting angle chosen randomly from a given distribution. Both models showed that mixed wettability can have significant influence on the rheology in terms of the dependence of the global volumetric flow rate on the global pressure drop. In the capillary fiber bundle model, for small pressure drops when only a small fraction of the tubes were open, it was found that the volumetric flow rate depended on the excess pressure drop as a power law with an exponent equal to 3/2 or 2 depending on the minimum pressure drop necessary for flow. When all the tubes were open due to a high pressure drop, the volumetric flow rate depended linearly on the pressure drop, independent of the wettability. In the transition region in between where most of the tubes opened, the volumetric flow depended more sensitively on the wetting angle distribution function and was in general not a simple power law. The dynamic pore network model results also showed a linear dependence of the flow rate on the pressure drop when the pressure drop is large. However, out of this limit the dynamic pore network model demonstrated a more complicated behavior that depended on the mixed wettability condition and the saturation. In particular, the exponent relating volumetric flow rate to the excess pressure drop could take on values anywhere between 1.0 and 1.8. The values of the exponent were highest for saturations approaching 0.5, also, the exponent generally increased when the difference in wettability of the two fluids were larger and when this difference was present for a larger fraction of the porous network.

Background High myopia refers to ametropia characterized by elongation of optic axis and retinal and choroidal degeneration in the fundus, and high myopic patients present with various degrees of structural and functional retinal changes and thinning of the retinal thickness. Active screening for high myopic patients with imaging techniques by OCT, Fundus photography and amsler grid and providing patients with corresponding treatment on this basis may effectively reduce the risk of irreversible visual function damage. Objective To point to the importance of combining OCT, fundus photography with amsler grid testing in patients with high myopia. Methods This study was conducted on 40 eyes from EI-Demerdash Hospital outpatient clinic and ophthalmology department investigations unit in the period from June 2021 till May 2022; the patients were 24 females (60.0%) and 16 males (40.0%) with age ranged from 16 to 50 years. Results Our investigations gave us an idea about the macular diseases associated with high myopia and the importance of combining use of OCT, fundus imaging and amsler grid with patients with high myopia. These findings consist of chorioretinal atrophy, foveoschisis, choroidal neovascularization, dehiscence of retinal layers known as retinoschisis (foveoschisis or extre foveoschisis), macular holes (lamellar and full thickness), posterior retinal detachment. We can early detect theses changes in macula of highly myopic patients by Amsler grid testing as daily self monitoring and this can be useful in directing the patient to more sophisticated investigation tool like OCT and fundus photography and therefore give the patient the optimal chance to save the vision. Conclusion OCT combined with fundus photography and amsler grid testing are helpful tools that allow detailed evaluation of the macula in eyes with pathological myopia and easily anticipate, diagnose and follow up diseases associated with high myopia.