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Attitudes towards diagnostic tests and therapies for dry eye disease

Graham JE, McGilligan VE, Berrar D, Leccisotti A, Moore JE, Bron AJ, et al.

Ophthalmic Res, 2010;43(1):11-7.
PMID: 19829007 DOI: 10.1159/000246573

AIM: The purpose of this study was to survey the attitudes of optometrists and ophthalmologists, located in a number of different countries, towards diagnostic tests and therapies for dry eye disease.
METHODS: A web-based questionnaire was used to survey attitudes using forced-choice questions and Likert scales.
RESULTS: Sixty-one respondents (23 ophthalmologists and 38 optometrists) reported a wide range of patient dry eye symptoms. A large variation in use of diagnostic tests was noted. Patient symptoms and fluorescein staining were reported to be significantly more valuable and more frequently performed than any other test. Artificial tear supplements and improved lid hygiene were the preferred therapeutic options selected by the entire group. The results demonstrated a wide variation in attitudes in relation to satisfaction with the range of available diagnostic and therapeutic options.
CONCLUSIONS: This study indicates that the interest for the issue of dry eye is relatively limited amongst eye professionals, as demonstrated by the poor participation in the questionnaire.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological/psychology
Fulltext Agreement analysis of LENSTAR with other techniques of biometry

Jasvinder S, Khang TF, Sarinder KK, Loo VP, Subrayan V

Eye (Lond), 2011 Jun;25(6):717-24.
PMID: 21394115 DOI: 10.1038/eye.2011.28

To assess the agreement of the optical low-coherence reflectometry (OLCR) device LENSTAR LS900 with partial coherence interferometry (PCI) device IOLMaster and applanation and immersion ultrasound biometry.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological*
Fulltext Grading of tarsal conjunctiva of young adults in Malaysia

Ishak B, Mohd-Ali B, Mohidin N

Clin Exp Optom, 2011 Sep;94(5):458-63.
PMID: 21793918 DOI: 10.1111/j.1444-0938.2011.00621.x

BACKGROUND: The monitoring and assessment of the upper tarsal conjunctiva can be enhanced by the use of a grading scale. The aim of this study was to categorise the appearance of normal tarsal conjunctiva among young adults in Malaysia using the Institute for Eye Research grading scale and to investigate inter- and intra-observer agreement.
METHODS: The appearance of the upper tarsal conjunctiva of 416 non-contact lens wearing subjects aged between 19 to 24 years was assessed by two separate observers for roughness and redness in three separate zones using the Institute for Eye Research grading scale. The average grade for each zone and overall grade for roughness and redness were calculated. Inter- and intra-observer agreements were analysed.
RESULTS: Subjects were categorised for ethnicity and the roughness and redness were calculated. No significant differences were found between Malay and Chinese eyes (p > 0.05). The average grades for the upper tarsal conjunctiva redness and roughness were 0.90 ± 0.25 and 0.86 ± 0.43, respectively. Significantly higher roughness scores were found in zone 1 compared to the other two zones (p = 0.03). Significant association was also found between tarsal conjunctiva redness and roughness (Spearman ρ= 0.45, p < 0.001). Correlation between redness and roughness with age (p = 0.48, p = 0.65) and gender (p = 0.30, p = 0.79) were not significant. Only 2.2 per cent of subjects had scores higher than 2.0 for roughness or redness. Inter- and intra-observer analysis showed good agreement between two observers during the study.
CONCLUSION: The roughness and redness of normal tarsal conjunctiva among young adults in Malaysia were found to be less than two units. Results of this study might be beneficial in clinical trials using contact lenses where changes in the tarsal conjunctiva are commonly used as an outcome measure.
Study site: Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia

Matched MeSH terms: Diagnostic Techniques, Ophthalmological/standards*; Diagnostic Techniques, Ophthalmological/statistics & numerical data
Fulltext Risk factors for diabetic retinopathy in diabetic screened using fundus photography at a primary health care setting in East Malaysia

Mallika PS, Lee PY, Cheah WL, Wong JS, Syed Alwi SAR, Nor Hayati H, et al.

Malays Fam Physician, 2011;6(2):60-65.
MyJurnal

Introduction: This study reports on the prevalence of diabetic retinopathy (DR) and risk factors among diabetic patients, who underwent fundus photography screening in a primary care setting of Borneo Islands, East Malaysia. We aimed to explore the preliminary data to help in the planning of more effective preventive strategies of DR at the primary health care setting.
Materials and Methods: A cross-sectional study on 738 known diabetic patients aged 19-82 years was conducted in 2004. Eye examination consists of visual acuity testing followed by fundus photography for DR assessment. The fundus pictures were reviewed by a family physician and an ophthalmologist. Fundus photographs were graded as having no DR, NPDR, PDR and maculopathy. The data of other parameters was retrieved from patient’s record. Bi-variate and multivariate analysis was used to elucidate the factors associated with DR.
Results: Any DR was detected in 23.7% (95% CI=21 to 27%) of the patients and 3.2% had proliferative DR. The risk factors associated with any DR was duration of DM (OR =2.5, CI=1.6 to 3.9 for duration of five to 10 years when compared to <5 years) and lower BMI (OR=1.8, CI=1.1 to 3.0). Moderate visual loss was associated with DR (OR=2.1, CI=1.2 to 3.7).
Conclusions: This study confirms associations of DR with diabetic duration, body mass index and visual loss. Our data provide preliminary findings to help to improve the screening and preventive strategies of DR at the primary health care setting.
Keywords: Diabetic retinopathy, epidemiology, screening, primary health care, Malaysia
Study site: Klinik Kesihatan Jalan Masjid, Kuching, Sarawak, Malaysia

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Prevalence and risk factors of epiretinal membrane in Asian Indians

Koh V, Cheung CY, Wong WL, Cheung CM, Wang JJ, Mitchell P, et al.

Invest Ophthalmol Vis Sci, 2012 Feb;53(2):1018-22.
PMID: 22247478 DOI: 10.1167/iovs.11-8557

To describe the prevalence of epiretinal membrane (ERM) and its risk factors in an Indian population and compare the findings with other populations.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Darkroom prone provocative test in primary angle closure glaucoma relatives

Zahari M, Ong YM, Taharin R, Ramli N

Optom Vis Sci, 2014 Apr;91(4):459-63.
PMID: 24637481 DOI: 10.1097/OPX.0000000000000220

To evaluate ocular biometric parameters and darkroom prone provocative test (DPPT) in family members of primary angle closure (PAC) glaucoma (PACG) patients and to establish any correlation between these biometric parameters and the DPPT response.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological*
Fulltext Bone Remodeling in Choroidal Osteoma Monitored by Fundus Photography and Spectral-Domain Optical Coherence Tomography

Kamalden TA, Lingam G, Sundar G

Ocul Oncol Pathol, 2014 Oct;1(1):13-8.
PMID: 27175357 DOI: 10.1159/000363454

Choroidal osteoma is a benign ossifying tumor of the choroid, consisting of mature bone tissue. It has been described to enlarge and evolve at varying rates over time. Here, we report and quantify the progression of a unilateral choroidal osteoma in a 7-year-old boy by fundus photography, and document tumor remodeling by spectral domain optical coherence tomography images.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Mookiah MR, Acharya UR, Koh JE, Chandran V, Chua CK, Tan JH, et al.

Comput Biol Med, 2014 Oct;53:55-64.
PMID: 25127409 DOI: 10.1016/j.compbiomed.2014.07.015

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological*
Accutome PachPen handheld ultrasonic pachymeter: intraobserver repeatability and interobserver reproducibility by personnel of different training grades

Peyman M, Tai LY, Khaw KW, Ng CM, Win MM, Subrayan V

Int Ophthalmol, 2015 Oct;35(5):651-5.
PMID: 25189684 DOI: 10.1007/s10792-014-9989-6

To assess the intra-observer repeatability and inter-observer reproducibility of central corneal thickness (CCT) measurements of PachPen (Accutome, Inc., Pennsylvania, USA), a hand-held, portable ultrasonic pachymeter when used by an ophthalmic nurse compared to an ophthalmologist. Ophthalmology Clinic, University of Malaya Medical Center In this prospective study, CCT was measured in 184 eyes of 92 healthy subjects, first by a corneal surgeon experienced in ultrasound pachymetry (Observer 1) followed by an ophthalmic nurse new to the procedure (Observer 2). Nine measurements were obtained from each eye by each observer, independently. Measurements were compared between the observers. Coefficients of repeatability and reproducibility were calculated. The Bland-Altman plot was used to assess agreement between observers. Mean age of the study population was 54.3 ± 15.2 years old and consisted of 43.5% male. Mean CCT as measured by Observers 1 and 2 were 528.3 ± 32.9 and 530.7 ± 33.3 µm, respectively. Observer 1 showed higher repeatability of measurements compared to that of Observer 2 (coefficient of repeatability 3.46 vs. 5.55%). The measurements by both observers showed high correlation (0.96) and good agreement (mean difference -2.4 µm; 95% limits of agreement -21.4, 16.7 µm). Coefficient of reproducibility of measurements between observers was 5.08%. Accutome PachPen hand-held ultrasound pachymeters gives excellent intra-observer repeatability and inter-observer reproducibility by personnel of different training grades.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological/instrumentation*
Application of higher-order spectra for automated grading of diabetic maculopathy

Mookiah MR, Acharya UR, Chandran V, Martis RJ, Tan JH, Koh JE, et al.

Med Biol Eng Comput, 2015 Dec;53(12):1319-31.
PMID: 25894464 DOI: 10.1007/s11517-015-1278-7

Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region. DME can be identified by manual examination of fundus images. It is laborious and resource intensive. Hence, in this work, automated grading of DME is proposed using higher-order spectra (HOS) of Radon transform projections of the fundus images. We have used third-order cumulants and bispectrum magnitude, in this work, as features, and compared their performance. They can capture subtle changes in the fundus image. Spectral regression discriminant analysis (SRDA) reduces feature dimension, and minimum redundancy maximum relevance method is used to rank the significant SRDA components. Ranked features are fed to various supervised classifiers, viz. Naive Bayes, AdaBoost and support vector machine, to discriminate No DME, NCSME and clinically significant macular edema classes. The performance of our system is evaluated using the publicly available MESSIDOR dataset (300 images) and also verified with a local dataset (300 images). Our results show that HOS cumulants and bispectrum magnitude obtained an average accuracy of 95.56 and 94.39% for MESSIDOR dataset and 95.93 and 93.33% for local dataset, respectively.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Contrast media induced transient cortical blindness

Balasingam S, Azman RR, Nazri M

QJM, 2016 Feb;109(2):121-2.
PMID: 26101228 DOI: 10.1093/qjmed/hcv121
Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Automated extraction of retinal vasculature

Tan JH, Acharya UR, Chua KC, Cheng C, Laude A

Med Phys, 2016 May;43(5):2311.
PMID: 27147343 DOI: 10.1118/1.4945413

The authors propose an algorithm that automatically extracts retinal vasculature and provides a simple measure to correct the extraction. The output of the method is a network of salient points, and blood vessels are drawn by connecting the salient points using a centripetal parameterized Catmull-Rom spline.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features

Acharya UR, Mookiah MR, Koh JE, Tan JH, Noronha K, Bhandary SV, et al.

Comput Biol Med, 2016 06 01;73:131-40.
PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009

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.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies

Koh JEW, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Sree SV, et al.

Comput Biol Med, 2017 05 01;84:89-97.
PMID: 28351716 DOI: 10.1016/j.compbiomed.2017.03.008

Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Blood vessel segmentation in color fundus images based on regional and Hessian features

Shah SAA, Tang TB, Faye I, Laude A

Graefes Arch Clin Exp Ophthalmol, 2017 Aug;255(8):1525-1533.
PMID: 28474130 DOI: 10.1007/s00417-017-3677-y

PURPOSE: To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis.
METHODS: Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel.
RESULTS: The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy.
CONCLUSIONS: Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological*
A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images

Acharya UR, Bhat S, Koh JEW, Bhandary SV, Adeli H

Comput Biol Med, 2017 Sep 01;88:72-83.
PMID: 28700902 DOI: 10.1016/j.compbiomed.2017.06.022

Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Iterative variational mode decomposition based automated detection of glaucoma using fundus images

Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR

Comput Biol Med, 2017 Sep 01;88:142-149.
PMID: 28728059 DOI: 10.1016/j.compbiomed.2017.06.017

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological*
Six-Year Incidence of Age-Related Macular Degeneration in Asian Malays: The Singapore Malay Eye Study

Cheung CMG, Ong PG, Neelam K, Tan PC, Shi Y, Mitchell P, et al.

Ophthalmology, 2017 09;124(9):1305-1313.
PMID: 28501376 DOI: 10.1016/j.ophtha.2017.03.056

PURPOSE: To determine the 6-year incidence of early and late age-related macular degeneration (AMD) in a Singaporean Malay population and to validate the Age-Related Eye Disease Study (AREDS) simplified severity scale in Asians.
DESIGN: Prospective, population cohort study.
PARTICIPANTS: The Singapore Malay Eye Study baseline participants (age, ≥40 years; 2006-2008) were followed up in 2011 through 2013, and 1901 of 3280 of eligible participants (72.1%) took part.
METHODS: Fundus photographs were graded using the Wisconsin AMD grading system.
MAIN OUTCOME MEASURES: Incidence of early and late AMD.
RESULTS: Gradable fundus photographs were available for 1809 participants who attended both baseline and 6-year follow-up examinations. The age-standardized incidences of early and late AMD were 5.89% (95% confidence interval [CI], 4.81-7.16) and 0.76% (95% CI, 0.42-1.29), respectively. The 5-year age-standardized incidence of early AMD (calculated based on the 6-year incidence) was lower in our population (5.58%; 95% CI, 4.43-7.01) compared with the Beaver Dam Eye Study population (8.19%). The incidence of late AMD in our population was similar to that of the Beaver Dam Eye Study population (0.98% [95% CI, 0.49-1.86] vs. 0.91%), the Blue Mountains Eye Study population (1.10% [95% CI, 0.52-9.56] vs. 1.10%), and the Hisayama Study population (1.09% [95% CI, 0.54-4.25] vs. 0.84%). The incidence of late AMD increased markedly with increasing baseline AREDS score (step 0, 0.23%; step 4, 9.09%).
CONCLUSIONS: This study documented the incidence of early and late AMD in a Malay population. The AREDS simplified severity scale is useful in predicting the risk of late AMD development in Asians.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological
A case report on allopurinol induced crystalline maculopathy

Cheah CK, Vijaya Singham N, Gun SC

Int J Rheum Dis, 2017 Dec;20(12):2253-2255.
PMID: 26864240 DOI: 10.1111/1756-185X.12827
Matched MeSH terms: Diagnostic Techniques, Ophthalmological
Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques

Koh JEW, Ng EYK, Bhandary SV, Hagiwara Y, Laude A, Acharya UR

Comput Biol Med, 2018 01 01;92:204-209.
PMID: 29227822 DOI: 10.1016/j.compbiomed.2017.11.019

Untreated age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma may lead to irreversible vision loss. Hence, it is essential to have regular eye screening to detect these eye diseases at an early stage and to offer treatment where appropriate. One of the simplest, non-invasive and cost-effective techniques to screen the eyes is by using fundus photo imaging. But, the manual evaluation of fundus images is tedious and challenging. Further, the diagnosis made by ophthalmologists may be subjective. Therefore, an objective and novel algorithm using the pyramid histogram of visual words (PHOW) and Fisher vectors is proposed for the classification of fundus images into their respective eye conditions (normal, AMD, DR, and glaucoma). The proposed algorithm extracts features which are represented as words. These features are built and encoded into a Fisher vector for classification using random forest classifier. This proposed algorithm is validated with both blindfold and ten-fold cross-validation techniques. An accuracy of 90.06% is achieved with the blindfold method, and highest accuracy of 96.79% is obtained with ten-fold cross-validation. The highest classification performance of our system shows the potential of deploying it in polyclinics to assist healthcare professionals in their initial diagnosis of the eye. Our developed system can reduce the workload of ophthalmologists significantly.

Matched MeSH terms: Diagnostic Techniques, Ophthalmological*

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