Abstract
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.
Highlights
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)
The performance of the proposed method was evaluated on a dataset of 41 IR images, which are described in the methodology section
The dataset consists of 18 eyes of control subjects who had no sign of Diabetic Retinopathy (DR) or diabetic macular oedema (DME) and 23 eyes with clinically diagnosed ME
Summary
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). Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. 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. The gray-level co-occurrence matrix (GLCM) for obtaining the texture features were introduced by Haralick in 1973, and this has been widely used in retinal image analyses[41,42]. One option is to use the IR image of the retina, which does not require a flash of light and is routinely performed during the step before OCT. Detecting pathologies, even in the presence of haemorrhages and cataracts, which may go undetected under other imaging systems[50,51,52]
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