![]() The proposed method yields a sensitivity and specificity of 80% and 70%, respectively 20. used a modified combined local binary pattern to extract local gray-level features of all channels and then a support vector machine (SVM) classifier to classify DME. 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. Previously, a number of methods have been proposed for grading diabetic macular oedema (DME) based on the location and segmentation of exudates 13, 25, 38 and macula or on the extraction of texture or image-based features 23, 40.Ī texture analysis is performed by extracting the statistical feature sets from the local distributions, which can be used later for segmentation or classification purposes. Some of the recent advancements in the field include the use of hyperspectral imaging and infrared imaging 3. The diagnosis and monitoring of ME require retinal imaging here, the three routinely used modalities are as follows: colour fundus photography (FP), fluorescein angiography (FA) and optical coherence tomography (OCT). Early diagnosis and monitoring of ME can decrease the risk of vision loss. ME is irreversible and is the major cause of a decrease in visual acuity in patients with diabetes 2. Macular edema (ME) refers to swelling within the retinal tissues that occurs when damaged blood vessels leak fluid and protein deposits into the macula region, leading to tissue thickening and distorting vision 1. 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 performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. 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) (A Z) value. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. ![]() A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. 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).
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