Retinal Image Matching Using Hierarchical Vascular Features

We propose a method for retinal image matching that can be used in image matching for person identification or patient longitudinal study. Vascular invariant features are extracted from the retinal image, and a feature vector is constructed for each of the vessel segments in the retinal blood vessels. The feature vectors are represented in a tree structure with maintaining the vessel segments actual hierarchical positions. Using these feature vectors, corresponding images are matched. The method identifies the same vessel in the corresponding images for comparing the desired feature(s). Initial results are encouraging and demonstrate that the proposed method is suitable for image matching and patient longitudinal study

Hypertensive eye disease

Hypertensive eye disease includes a spectrum of pathological changes, the most well known being hypertensive retinopathy. Other commonly involved parts of the eye in hypertension include the choroid and optic nerve, sometimes referred to as hypertensive choroidopathy and hypertensive optic neuropathy. Together, hypertensive eye disease develops in response to acute and/or chronic elevation of blood pressure. Major advances in research over the past three decades have greatly enhanced our understanding of the epidemiology, systemic associations and clinical implications of hypertensive eye disease, particularly hypertensive retinopathy. Traditionally diagnosed via a clinical funduscopic examination, but increasingly documented on digital retinal fundus photographs, hypertensive retinopathy has long been considered a marker of systemic target organ damage (for example, kidney disease) elsewhere in the body. Epidemiological studies indicate that hypertensive retinopathy signs are commonly seen in the general adult population, are associated with subclinical measures of vascular disease and predict risk of incident clinical cardiovascular events. New technologies, including development of non-invasive optical coherence tomography angiography, artificial intelligence and mobile ocular imaging instruments, have allowed further assessment and understanding of the ocular manifestations of hypertension and increase the potential that ocular imaging could be used for hypertension management and cardiovascular risk stratification

Classification of SD-OCT Volumes using Local Binary Patterns: Experimental Validation for DME Detection

International audienceThis paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various pre-processings in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and non-linear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of pre-processing is inconsistent with respect to different classifiers and feature configurations

Sleep Duration and Diabetic Kidney Disease

Aims: Abnormally short or long durations of sleep have been proposed as a risk factors for diabetes and its micro- and macro-vascular complications. However, the relationship between sleep duration and diabetic kidney disease (DKD) has not been well-characterized. Thus, we aimed to examine the association of sleep duration with DKD in two Asian populations.Methods: We included 1,258 persons (Malay, n = 403; Indian, n = 855) aged 40–80 years with diabetes from a population-based cross-sectional sample from Singapore. DKD was defined by low estimated glomerular filtration rate (eGFR <60 mL/min/1.73 m2) and albuminuria (urinary albumin-to-creatinine ratio ≥30 mg/g, only measured in Indian participants). Self-reported habitual sleep duration was categorized into 4 categories: very short (<5 h), short (5–6.9 h), normal (7–8 h) and long (>8 h). The associations of sleep duration with low eGFR and albuminuria were analyzed using multivariable logistic regression models adjusted for multiple potential confounders (including classic risk factors such as HbA1c and hypertension).Results: In total, 268 (21.3%) participants had low eGFR, and 271 (34.7% in Indians) had albuminuria. The number (%) of individuals with very short, short, normal, and long durations of sleep were 117 (9.3%), 629 (50.0%), 429 (34.1%), and 83 (6.6%), respectively. Long sleep duration was associated with a higher odds of renal insufficiency compared to normal sleep duration (OR [95% CI]: 2.31 [1.27–4.19]) on multivariable analysis. Similarly, both long and very short durations of sleep were associated with higher odds of albuminuria (OR [95%]: 2.44 [1.36, 4.38] and 2.37 [1.25, 4.50], respectively) in Indian participants (where data on albuminuria were available).Conclusions: Our study suggests that abnormally short or long durations of sleep were associated with DKD, manifesting as either a reduced eGFR or increased albuminuria. However, further longitudinal data would be required for confirmation

Serum Apolipoprotein AI and B Are Stronger Biomarkers of Diabetic Retinopathy Than Traditional Lipids

10.2337/dc10-0793Diabetes Care342474-479DICA

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections

International audienceThis paper deals with the automated detection of DME on OCT volumes.Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan.Features such as HoG and LBP are extracted and combined to create a set of different feature vectors which are fed to a linear-SVM classifier.Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset

Is Routine Pupil Dilation Safe among Asian Patients with Diabetes?

PURPOSE. To investigate the risk of acute angle closure (AAC), changes in intraocular pressure (IOP), and factors associated with these outcomes after routine pupil dilation in a cohort of Asian subjects with diabetes mellitus. METHODS. The study was a prospective observational case series of 1910 consecutive Asian subjects newly referred for assessment of diabetic retinopathy at a tertiary clinic. All subjects underwent routine pupil dilation unless there was a prior history of angle-closure glaucoma. Noncontact air-puff tonometry was used to assess IOP, which was measured by the same observer before and 1 hour after pupil dilation. Subjects were assessed for signs and symptoms of AAC before leaving the clinic, and their charts were also subsequently reviewed for revisits with AAC. RESULTS. Of the 1910 subjects who participated, none developed AAC. Sixty-nine subjects (3.6%, 95% CI: 2.8%-4.5%) showed an increase in IOP of Ն5 mm Hg in the either eye, 37 subjects (1.9%, 95% CI: 1.4%-2.6%) had a postdilation IOP Ͼ25 mm Hg in either eye, and only 10 subjects (0.52%, 95% CI: 0.25%-0.96%) had an increase in IOP Ն5 mm Hg and had a postdilation IOP Ͼ25 mm Hg in either eye. The level of predilation IOP and a known history of glaucoma were significant risk factors for a postdilation IOP Ն25 mm Hg. CONCLUSIONS. In this cohort of Asian persons with diabetes, the risk of AAC was insignificant after routine dilation of pupils for fundus examination. These data substantiate the safety of routine dilation of pupils in Asian patients with diabetes. (Invest Ophthalmol Vis Sci. 2009;50:4110 -4113

Concordance between SIVA, IVAN, and VAMPIRE software tools for semi-automated analysis of retinal vessel caliber

We aimed to compare measurements from three of the most widely used software packages in the literature and to generate conversion algorithms for measurement of the central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE) between SIVA and IVAN and between SIVA and VAMPIRE. We analyzed 223 retinal photographs from 133 human participants using both SIVA, VAMPIRE and IVAN independently for computing CRAE and CRVE. Agreement between measurements was assessed using Bland–Altman plots and intra-class correlation coefficients. A conversion algorithm between measurements was carried out using linear regression, and validated using bootstrapping and root-mean-square error. The agreement between VAMPIRE and IVAN was poor to moderate: The mean difference was 20.2 µm (95% limits of agreement, LOA, −12.2–52.6 µm) for CRAE and 21.0 µm (95% LOA, −17.5–59.5 µm) for CRVE. The agreement between VAMPIRE and SIVA was also poor to moderate: the mean difference was 36.6 µm (95% LOA, −12.8–60.4 µm) for CRAE, and 40.3 µm (95% LOA, 5.6–75.0 µm) for CRVE. The agreement between IVAN and SIVA was good to excellent: the mean difference was 16.4 µm (95% LOA, −4.25–37.0 µm) for CRAE, and 19.3 µm (95% LOA, 0.09–38.6 µm) for CRVE. We propose an algorithm converting IVAN and VAMPIRE measurements into SIVA-estimated measurements, which could be used to homogenize sets of vessel measurements obtained with different software packages