The receptor for advanced glycation end-products (RAGE) has been implicated in the pathogenesis of diabetic microvascular complications. The aim of this study was to investigate the association between 2245G/A gene polymorphism of the RAGE gene and retinopathy in Malaysian type 2 diabetic patients.
The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.
Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.
Diabetic nephropathy (DN) has become the most common cause of end-stage renal failure. Early referral and specific nephrology treatment could delay the disease progression and should reduce the treatment cost, mortality and morbidity rate in these patients. This is a single-center, retrospective review of all DN patients referred to the nephrology clinic in Hospital Sultan Ahmad Shah, Temerloh, from 2000 to 2009, to study and define the clinical characteristics of DN patients at the time of the referral to the nephrology clinic. A total of 75 patient case records were reviewed. Forty-three (57.3%) of them were males, with a median age of 64.3 ± 8.5 years at the time of referral. Only 14.7% of them had blood pressure lower than 125/75 mmHg. Co-morbid and disease-related complications were also commonly diagnosed and 28.4% (n = 21) had ischemic heart disease, 23% (n = 17) had diabetic retinopathy and 20.3% (n = 15) had diabetic neuropathy. The mean serum creatinine at the time of referral was 339.8 ± 2.3 μmol/L, gylcated hemoglobin A 1c (HbA1C) was 8.1 ± 2.0 %, serum fasting glucose was 9.6 ± 4.7 mmol/L, serum cholesterol was 5.4 ± 1.2 mmol/L and hemoglobin level was 10.6 ± 2.9 g/dL. Although female patients were less frequently seen in the early stages of chronic kidney disease (CKD), they comprised at least 72.7% of CKD stage 5 (male:female; 6:16, P <0.05). Twenty-nine percent (n=22) of them were referred at CKD stage 5, 48% (n=36) were at CKD stage 4, 17.3% (n=13) were at CKD stage 3, 4% (n=3) were at CKD stage 2 and 1.3% (n=1) was at CKD stage 1. Advanced CKD patients were frequently prescribed with more antihypertensives. CKD stage 5 patients were prescribed with two-and-half types of antihypertensive as compared to two types of anti-hypertensive in CKD stage 2 and stage 3. Furthermore, ACE-inhibitors (ACE-I) were less frequently prescribed to them. Only 22.7% (n=5) of CKD stage 5 patients received ACE-I and 30% (n=11) in CKD stage 4 patients as compared to 53.4% (n=7) in CKD patients stage 3. This review shows that DN patients were referred late to the nephrologists and the overall disease management was suboptimal. Antihypertensive requirement was also increased and ACEIs were less frequently prescribed in the advanced diabetic nephropathy patients.
Study site: Nephrology Clinic, Hospital Sultan Ahmad Shah, Temerloh, Pahang, Malaysia
A 36 year-old Malay lady with diabetes mellitus in pregnancy and poorly controlled hypertension developed rapid progression of diabetic retinopathy from no retinopathy to florid proliferative retinopathy over three months in her right eye. She had subsequent loss of vision due to vitreous haemorrhage in the peri-partum period. She had good final visual acuity with quiescent retinopathy following pars planar vitrectomy. A similar course was avoided in the left eye by timely pan retinal photocoagulation.
AIM: To report maternal and fetal adverse outcomes, in spite of appropriate treatment and regular follow up, in diabetic pregnant women with proliferative diabetic retinopathy.
METHODS: Case series of four young pregnant diabetics aged between 20 and 25 years with type I diabetes mellitus and proliferative diabetic retrinopathy.
RESULTS: The maternal adverse outcomes were abortion in one patient, pre-eclampsia and preterm delivery in one patient, and renal failure requiring dialysis in one patient. The fetal adverse outcomes were neonatal death in one case and premature baby in another case.
CONCLUSION: These cases highlight the fact that diabetic pregnant women should be closely followed up by the obstetricians and physicians when they have proliferative retinopathy. The proliferative diabetic retinopathy should be considered as a part of the assessment when counseling a diabetic woman in antenatal check up and also in the follow up visits during pregnancy.
KEYWORDS: pregnancy; proliferative diabetic retinopathy; type I diabetes mellitus; vitreous haemorrhage
Study site: Eye clinic, University Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia
This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.
Diabetic retinopathy (DR) has become a serious threat in our society, which causes 45% of the legal blindness in diabetes patients. Early detection as well as the periodic screening of DR helps in reducing the progress of this disease and in preventing the subsequent loss of visual capability. This paper provides an automated diagnosis system for DR integrated with a user-friendly interface. The grading of the severity level of DR is based on detecting and analyzing the early clinical signs associated with the disease, such as microaneurysms (MAs) and hemorrhages (HAs). The system extracts some retinal features, such as optic disc, fovea, and retinal tissue for easier segmentation of dark spot lesions in the fundus images. That is followed by the classification of the correctly segmented spots into MAs and HAs. Based on the number and location of MAs and HAs, the system quantifies the severity level of DR. A database of 98 color images is used in order to evaluate the performance of the developed system. From the experimental results, it is found that the proposed system achieves 84.31% and 87.53% values in terms of sensitivity for the detection of MAs and HAs respectively. In terms of specificity, the system achieves 93.63% and 95.08% values for the detection of MAs and HAs respectively. Also, the proposed system achieves 68.98% and 74.91% values in terms of kappa coefficient for the detection of MAs and HAs respectively. Moreover, the system yields sensitivity and specificity values of 89.47% and 95.65% for the classification of DR versus normal.
Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.
Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. At present, the classification of DR is based on the International Clinical Diabetic Retinopathy Disease Severity. In this paper, FAZ enlargement with DR progression is investigated to enable a new and an effective grading protocol DR severity in an observational clinical study. The performance of a computerised DR monitoring and grading system that digitally analyses colour fundus image to measure the enlargement of FAZ and grade DR is evaluated. The range of FAZ area is optimised to accurately determine DR severity stage and progression stages using a Gaussian Bayes classifier. The system achieves high accuracies of above 96%, sensitivities higher than 88% and specificities higher than 96%, in grading of DR severity. In particular, high sensitivity (100%), specificity (>98%) and accuracy (99%) values are obtained for No DR (normal) and Severe NPDR/PDR stages. The system performance indicates that the DR system is suitable for early detection of DR and for effective treatment of severe cases.
Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. In this article, a computerised DR grading system, which digitally analyses retinal fundus image, is used to measure foveal avascular zone. A v-fold cross-validation method is applied to the FINDeRS database to evaluate the performance of the DR system. It is shown that the system achieved sensitivity of >84%, specificity of >97% and accuracy of >95% for all DR stages. At high values of sensitivity (>95%), specificity (>97%) and accuracy (>98%) obtained for No DR and severe NPDR/PDR stages, the computerised DR grading system is suitable for early detection of DR and for effective treatment of severe cases.
Diabetic Macular Edema (DME) is caused by accumulation of extracellular fluid from hyperpermeable capillaries within the macula. DME is one of the leading causes of blindness among Diabetes Mellitus (DM) patients. Early detection followed by laser photocoagulation can save the visual loss. This review discusses various imaging modalities viz. biomicroscopy, Fluorescein Angiography (FA), Optical Coherence Tomography (OCT) and colour fundus photographs used for diagnosis of DME. Various automated DME grading systems using retinal fundus images, associated retinal image processing techniques for fovea, exudate detection and segmentation are presented. We have also compared various imaging modalities and automated screening methods used for DME grading. The reviewed literature indicates that FA and OCT identify DME related changes accurately. FA is an invasive method, which uses fluorescein dye, and OCT is an expensive imaging method compared to fundus photographs. Moreover, using fundus images DME can be identified and automated. DME grading algorithms can be implemented for telescreening. Hence, fundus imaging based DME grading is more suitable and affordable method compared to biomicroscopy, FA, and OCT modalities.
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.
In view of the alarming increase in the incidence of diabetes mellitus in Malaysia, we conducted a study to assess the awareness of complications of diabetes among the diabetics attending the peripheral clinics in Melaka. The study period was from January 2007 to December 2007. 351 patients were included in the study. 79.8% were aware of the complications of diabetes mellitus and 87.2% were aware that diabetes can affect the eyes. However, only 50% of the patients underwent an ophthalmological evaluation. Although awareness was good, the motivation to undergo the assessment was poor.
Study site: Klinik Kesihatan Peringgit, Klinik Kommunity Ayer Keroh, Melaka, Malaysia
Conflicting results have been reported in different populations on the association between two particular RAGE gene polymorphisms (-429T/C and -374T/A) and retinopathy in diabetic patients. Therefore this study was designed to assess the association between both gene polymorphisms with retinopathy in Malaysian diabetic patients. A total of 342 type 2 diabetic patients [171 without retinopathy (DNR) and 171 with retinopathy (DR)] and 235 healthy controls were included in this study. Genomic DNA was obtained from blood samples and the screening for the gene polymorphisms was done using polymerase chain reaction-restriction fragment length polymorphism approach. Overall, the genotype distribution for both polymorphisms was not statistically different (p>0.05) among the control, DNR and DR groups. The -429C minor allele frequency of DR group (12.0%) was not significantly different (p>0.05) when compared to DNR group (16.1%) and healthy controls (11.3%). The -374A allele frequency also did not differ significantly between the control and DNR (p>0.05), control and DR (p>0.05) as well as DNR and DR groups (p>0.05). This is the first study report on RAGE gene polymorphism in Malaysian DR patients. In conclusion, -429T/C and -374T/A polymorphisms in the promoter region of RAGE gene were not associated with Malaysian type 2 DR patients.
AIM/HYPOTHESIS: The aim of our study was to characterize the human salivary proteome and determine the changes in protein expression in two different stages of diabetic retinopathy with type-2 diabetes mellitus: (1) with non-proliferative diabetic retinopathy (NPDR) and (2) with proliferative diabetic retinopathy (PDR). Type-2 diabetes mellitus without diabetic retinopathy (XDR) was designated as control.
METHOD: In this study, 45 saliva samples were collected (15 samples from XDR control group, 15 samples from NPDR disease group and 15 samples from PDR disease group). Salivary proteins were extracted, reduced, alkylated, trypsin digested and labeled with an isobaric tag for relative and absolute quantitation (iTRAQ) before being analyzed by an Orbitrap fusion tribrid mass spectrometer. Protein annotation, fold change calculation and statistical analysis were interrogated by Proteome Discoverer. Biological pathway analysis was performed by Ingenuity Pathway Analysis. Data are available via ProteomeXchange with identifiers PXD003723-PX003725.
RESULTS: A total of 315 proteins were identified from the salivary proteome and 119 proteins were found to be differentially expressed. The differentially expressed proteins from the NPDR disease group and the PDR disease group were assigned to respective canonical pathways indicating increased Liver X receptor/Retinoid X receptor (LXR/RXR) activation, Farnesoid X receptor/Retinoid X receptor (FXR/RXR) activation, acute phase response signaling, sucrose degradation V and regulation of actin-based motility by Rho in the PDR disease group compared to the NPDR disease group.
CONCLUSIONS/INTERPRETATION: Progression from non-proliferative to proliferative retinopathy in type-2 diabetic patients is a complex multi-mechanism and systemic process. Furthermore, saliva was shown to be a feasible alternative sample source for diabetic retinopathy biomarkers.
OBJECTIVE: This study aimed to assess the circulating levels of activated nuclear factor kappa B p65 and monocyte chemotactic protein-1 in diabetic retinopathy patients who were taking antihyperglycemic and antihypertensive drugs.
METHODS: In total, 235 healthy controls and 371 Type 2 diabetic patients [171 without retinopathy (DNR) and 200 patients with retinopathy (diabetic retinopathy)] were recruited for this study. Plasma and the nuclear fraction of peripheral blood mononuclear cells were isolated for the quantification of the monocyte chemotactic protein-1 and nuclear factor kappa B p65 levels, respectively.
RESULTS: Non-medicated diabetic retinopathy patients had significantly higher levels of activated nuclear factor kappa B p65 and plasma monocyte chemotactic protein-1 than DNR patients. Diabetic retinopathy patients who were taking antihyperglycemic and antihypertensive drugs showed significant reductions in both the nuclear factor kappa B p65 and monocyte chemotactic protein-1 levels compared with the non-medicated patients.
CONCLUSION: This study demonstrated the significant attenuation of both the nuclear factor kappa B p65 and circulating monocyte chemotactic protein-1 levels in diabetic retinopathy patients taking antihyperglycemic and antihypertensive drugs.
Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.
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.
Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.