Augustinus Laude

Polypoidal choroidal vasculopathy and neovascular age-related macular degeneration: Same or different disease?

Neovascular age-related macular degeneration (nAMD) is the commonest cause of severe visual impairment in older adults in Caucasian white populations. Polypoidal choroidal vasculopathy (PCV) has been described as a separate clinical entity differing from nAMD and other macular diseases associated with subretinal neovascularization. It remains controversial as to whether or not PCV represents a sub-type of nAMD. This article summarizes the current literature on the clinical, pathophysiological and epidemiological features and treatment responses of PCV and compares this condition to nAMD. Patients with PCV are younger and more likely Asians, and eyes with PCV lack drusen, often present with serosanguinous maculopathy or hemorrhagic pigment epithelial detachment, and have differing responses to photodynamic therapy and anti-vascular endothelial growth factor (VEGF) agents. There are also significant differences in angiographic and optical coherence tomography features between PCV and nAMD. Histopathological studies suggest differences in the anatomical details of the associated vascular abnormalities in the retina and choroids and the relative role of VEGF. There is emerging evidence of common molecular genetic determinants involving complement pathway and common environmental risk factors (e.g. smoking). Such information could further assist clinicians involved in the care of elderly patients with these conditions.

Computer-aided diagnosis of diabetic retinopathy: A review

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.

Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach

Human eye is one of the most sophisticated organ, with retina, pupil, iris cornea, lens and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. DR is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of DR are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources used for mass screening of DR. We present an automatic screening system for the detection of normal and DR stages (NPDR and PDR). The proposed systems involves processing of fundus images for extraction of abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture and entropies. Our protocol uses total of 156 subjects consisting of two stages of DR and normal. In this work, we have fed thirteen statistically significant (p<0.0001) features for Probabilistic Neural Network (PNN), Decision Tree (DT) C4.5, and Support Vector Machine (SVM) to select the best classifier. The best model parameter (@s) for which the PNN classifier performed best was identified using global optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). We demonstrated an average classification accuracy of 96.15%, sensitivity of 96.27% and specificity of 96.08% for @s=0.0104 using threefold cross validation using PNN classifier. The computer-aided diagnosis (CAD) results were validated by comparing with expert ophthalmologists. The proposed automated system can aid clinicians to make a faster DR diagnosis during the mass screening of normal/DR images.

Optical coherence tomography patterns as predictors of visual outcome in dengue-related maculopathy.

Purpose: The purpose of this study was to characterize the presentations, long-term outcomes, and visual prognostic factors in dengue-related maculopathy of 41 patients with dengue fever and impaired vision from dengue-related maculopathy in a retrospective noninterventional and observational series. Methods: The medical records of patients with dengue-related maculopathy diagnosed over 18 months between July 2004 and December 2005 at The Eye Institute, Tan Tock Seng Hospital and Communicable Disease Center, Singapore, were reviewed and followed up for 24 months. Visual acuity and symptoms (presence of scotoma on automated visual fields and Amsler grid) were correlated with optical coherence tomography evaluation. Results: Mean age was 28.7 years and there were more men (53.7%). The most common visual complaints were blurring of vision (51.2%) and central scotoma (34.1%). Most patients recovered best-corrected visual acuity >20/40. Optical coherence tomography showed 3 patterns of maculopathy: 1) diffuse retinal thickening; 2) cystoid macular edema; and 3) foveolitis. The visual outcome was independent of the extent of edema, but scotomata persisted longest in patients with foveolitis and shortest with those with diffuse retinal thickening. Conclusion: Dengue-associated ocular inflammation is an emerging ophthalmic condition and often involves the posterior segment. Prognosis is variable. Patients usually regain good vision but may retain persistent scotomata even at 2 years despite clinical resolution of the disease. Optical coherence tomography patterns in dengue maculopathy are useful for characterization, monitoring, and prognostication of the visual defect.

Intravitreal therapy for neovascular age-related macular degeneration and inter-individual variations in vitreous pharmacokinetics

This article aims to provide an interpretation and perspective on current concepts and recent literature regarding the evidence for individualizing intravitreal therapy (IVT), particularly considering iatrogenic and patient factors in the management of neovascular age-related macular degeneration (AMD). As ocular parameters that govern IVT pharmacokinetics do vary between individuals with AMD, developing a personalized strategy could improve safety and efficacy. This has to be derived from clinical measurements and data from laboratory animals; however, it is recognized that the animal models used in the development of intraocular formulations differ in their vitreous geometry from humans. These factors may be of relevance to the design of ophthalmic formulations and optimizing treatment outcomes in AMD. Further studies are needed to drive improvements in clinical practice which are aimed at maximizing the efficacy profile in IVT for AMD by a more rigorous evaluation of patient and surgeon-related variables.

Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index

Posterior Segment Eye Diseases (PSED) namely Diabetic Retinopathy (DR), glaucoma and Age-related Macular Degeneration (AMD) are the prime causes of vision loss globally. Vision loss can be prevented, if these diseases are detected at an early stage. Structural abnormalities such as changes in cup-to-disc ratio, Hard Exudates (HE), drusen, Microaneurysms (MA), Cotton Wool Spots (CWS), Haemorrhages (HA), Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in PSED can be identified by manual examination of fundus images by clinicians. However, manual screening is labour-intensive, tiresome and time consuming. Hence, there is a need to automate the eye screening. In this work Bi-dimensional Empirical Mode Decomposition (BEMD) technique is used to decompose fundus images into 2D Intrinsic Mode Functions (IMFs) to capture variations in the pixels due to morphological changes. Further, various entropy namely Renyi, Fuzzy, Shannon, Vajda, Kapur and Yager and energy features are extracted from IMFs. These extracted features are ranked using Chernoff Bound and Bhattacharyya Distance (CBBD), Kullback-Leibler Divergence (KLD), Fuzzy-minimum Redundancy Maximum Relevance (FmRMR), Wilcoxon, Receiver Operating Characteristics Curve (ROC) and t-test methods. Further, these ranked features are fed to Support Vector Machine (SVM) classifier to classify normal and abnormal (DR, AMD and glaucoma) classes. The performance of the proposed eye screening system is evaluated using 800 (Normal=400 and Abnormal=400) digital fundus images and 10-fold cross validation method. Our proposed system automatically identifies normal and abnormal classes with an average accuracy of 88.63%, sensitivity of 86.25% and specificity of 91% using 17 optimal features ranked using CBBD and SVM-Radial Basis Function (RBF) classifier. Moreover, a novel Retinal Risk Index (RRI) is developed using two significant features to distinguish two classes using single number. Such a system helps to reduce eye screening time in polyclinics or community-based mass screening. They will refer the patients to main hospitals only if the diagnosis belong to the abnormal class. Hence, the main hospitals will not be unnecessarily crowded and doctors can devote their time for other urgent cases.

Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features

Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.

Asian age‐related macular degeneration phenotyping study: rationale, design and protocol of a prospective cohort study

Background:  Current knowledge of the phenotypic characteristics (e.g. clinical features, risk factors, natural history and treatment response) of age‐related macular degeneration (AMD) in Asians remains limited. This report summarizes the rationale and study design of a prospective observational study of Asian neovascular AMD, including polypoidal choroidovasculopathy variant.

Local configuration pattern features for age-related macular degeneration characterization and classification

Age-related Macular Degeneration (AMD) is an irreversible and chronic medical condition characterized by drusen, Choroidal Neovascularization (CNV) and Geographic Atrophy (GA). AMD is one of the major causes of visual loss among elderly people. It is caused by the degeneration of cells in the macula which is responsible for central vision. AMD can be dry or wet type, however dry AMD is most common. It is classified into early, intermediate and late AMD. The early detection and treatment may help one to stop the progression of the disease. Automated AMD diagnosis may reduce the screening time of the clinicians. In this work, we have introduced LCP to characterize normal and AMD classes using fundus images. Linear Configuration Coefficients (CC) and Pattern Occurrence (PO) features are extracted from fundus images. These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz. Decision Tree (DT), Nearest Neighbour (k-NN), Naive Bayes (NB), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to classify normal and AMD classes. The performance of the system is evaluated using both private (Kasturba Medical Hospital, Manipal, India) and public domain datasets viz. Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) using ten-fold cross validation. The proposed approach yielded best performance with a highest average accuracy of 97.78%, sensitivity of 98.00% and specificity of 97.50% for STARE dataset using 22 significant features. Hence, this system can be used as an aiding tool to the clinicians during mass eye screening programs to diagnose AMD.

Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index

The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME. The fundus images are subjected to RT to obtain sinograms and DWT is applied on these sinograms to extract wavelet coefficients (approximate, horizontal, vertical and diagonal). DCT is applied on approximate coefficients to obtain 2D-DCT coefficients. Further, these coefficients are converted into 1D vector by arranging the coefficients in zig-zag manner. This 1D signal is subjected to locality sensitive discriminant analysis (LSDA). Finally, various supervised classifiers are used to classify the three classes using significant features. Our proposed technique yielded a classification accuracy of 100% and 97.01% using two and seven significant features for private and public (MESSIDOR) databases respectively. Also, a maculopathy index is formulated with two significant parameters to discriminate the three groups distinctly using a single integer. Hence, our obtained results suggest that this system can be used as an eye screening tool for diabetic subjects for DME.