Infrared retinal images for flashless detection of macular edema

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) (A Z) 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.

Thermal imaging method to evaluate childhood obesity based on machine learning techniques

“…They obtained 100% sensitivity, specificity, and accuracy using SVM classifier to classify ME from normal subjects. 14 Selvarani et al performed thermal imaging of tongue region on 25 subjects to diagnose diabetes in them. They applied bilinear and median filter to remove noise and sharpen the edges in the regional thermograms and finally mapping the thermal active region on tongue using Dyadic wavelet transform.…”