Infrared retinal images for flashless detection of macular edema.

This study evaluates the use of infrared (IR) images of the retina, obtained without flashes of light, for machine-based detection of macular oedema (ME). A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. The results from the one-way ANOVA indicated there was a significant difference between ME eyes and the controls when using GLCM features, with the correlation feature having the highest area under the curve (AUC) (AZ) value. The performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. This research shows that the texture of the IR images of the retina has a significant difference between ME eyes and the controls and that it can be considered for machine-based detection of ME without requiring flashes of light.

Association between Optical Coherence Tomography and Fluorescein Angiography based retinal features in the diagnosis of Macular Edema.

The association between optical coherence tomography (OCT) and the geometrical vascular parameters obtained from the fluorescein angiography (FA) of the eyes with macular edema (ME) was investigated. Data from 82 untreated eyes with ME were studied. Fractal dimension (FD), simple tortuosity, branching angle, total angle count and vessel to background ratio were the five vasculature parameters from FA that were studied. The four OCT features measured were central retinal/foveal thickness, average para-fovea thickness, average peri-fovea thickness and OCT volume. The four OCT parameters showed a significant difference between ME requiring treatment (MERT) and non-MERT eyes with the central retinal thickness (threshold at 300 µm) and average para-fovea thickness (threshold at 338.5 µm) as most significant. The results also indicate that FD from the FA of retinal vessels in the macular region was associated with the changes in retinal thickness and that OCT parameters can potentially be used for directly identifying ME.

Agreement study between color and IR retinal images based on retinal vasculature morphological parameters.

BACKGROUND: Color fundus photography have been extensively used to explore the link between retinal morphology changes associated with various disease i.e. Diabetic Retinopathy, Glaucoma. The development of multimodal imaging system that integrates Infrared Scanning Laser Ophthalmoscope (IR-SLO) and Optical Coherence Tomography (OCT) could help in studying these diseases at an early stage. The aim of this study was to test the agreement between the retinal vasculature parameters from the Infrared images obtained from optical coherence tomography and color fundus imaging. METHODS: The IR and Color retinal images were obtained from 16 volunteer participants and seven retinal vessel parameters, i.e. Fractal Dimension (FD), Average Angle (ABA), Total Angle Count (TAC), Tortuosity (ST), Vessel/Background ratio (VBR), Central Retinal Arteriolar Equivalent (CRAE) and Central Retinal Venular Equivalent (CRVE) were extracted from these retinal images using Retinal Image Vasculature Assessment software (RIVAS) and Integrative Vessel Analysis (IVAN). RESULTS: The Bland Altman plot was used to investigate the agreement between the two modalities. The paired sample t-test was used to assess the presence of fixed bias and the slope of Least Square Regression (LSR) line for the presence of proportional bias. The paired sample t-test showed that there was no statistically significant difference between Color and IR based on retinal vessel features (all p values > 0.05). LSR also revealed no statistically significant difference in the retinal vessel features between Color and IR. CONCLUSION: This study has revealed that there is a fair agreement between Color and IR images based on retinal vessel features. This research has shown that it is possible to use IR images of the retina to measure the retinal vasculature parameters which has the advantage of being flash-less, can be used even if there is opacity due to cataract, and can be performed along with OCT on the same device.

Relation Between Retinal Vasculature and Retinal Thickness in Macular Edema.

This study has investigated the relationship of the retinal vasculature and the retinal thickness for Macular Edema (ME) subjects. Ninety sets of Fluorescein Angiograph (FA) and Optical Coherence Tomography (OCT) of 54 participants were analyzed. Multivariate analysis using the binary logistic regression model was used to study the association between vessel parameters and retinal thickness. The results reveal retinal vessel feature i.e. fractal dimension (FD) as the most sensitive parameter to the changes in retinal thickness associated with ME. Thus, indicating a direct relationship between the retinal vasculature and retinal thickness which is caused due to neovascular causing exudates, leakages and hemorrhages, with applications for alternate modality for detection of ME.

A novel method for segmentation of Infrared Scanning Laser Ophthalmoscope (IR-SLO) images of retina.

Retinal vessel segmentation forms an essential element of automatic retinal disease screening systems. The development of multimodal imaging system with IR-SLO and OCT could help in studying the early stages of retinal disease. The advantages of IR-SLO to examine the alterations in the structure of retina and direct correlation with OCT can be useful for assessment of various diseases. This paper presents an automatic method for segmentation of IR-SLO fundus images based on the combination of morphological filters and image enhancement techniques. As a first step, the retinal vessels are contrasted using morphological filters followed by background exclusion using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Bilateral filtering. The final segmentation is obtained by using Isodata technique. Our approach was tested on a set of 26 IR-SLO images and results were compared to two set of gold standard images. The performance of the proposed method was evaluated in terms of sensitivity, specificity and accuracy. The system has an average accuracy of 0.90 for both the sets.