ABSTRACT.Purpose: To investigate the use of automated image analysis for the detection of diabetic retinopathy (DR) in fundus photographs captured with and without pharmacological pupil dilation using a digital non-mydriatic camera. Methods: A total of 83 patients (165 eyes) with type 1 or type 2 diabetes, representing the full spectrum of DR, were photographed with and without pharmacological pupil dilation using a digital non-mydriatic camera. Two sets of five overlapping, nonstereoscopic, 45-degree field images of each eye were obtained. All images were graded in a masked fashion by two readers according to ETDRS standards and disagreements were settled by an independent adjudicator. Automated detection of red lesions as well as image quality control was made: detection of a single red lesion or insufficient image quality was categorized as possible DR. Results: At patient level, the automated red lesion detection and image quality control combined demonstrated a sensitivity of 89.9% and specificity of 85.7% in detecting DR when used on images captured without pupil dilation, and a sensitivity of 97.0% and specificity of 75.0% when used on images captured with pupil dilation. For moderate non-proliferative or more severe DR the sensitivity was 100% for images captured both with and without pupil dilation. Conclusion: Our results demonstrate that the described automated image analysis system, which detects the presence or absence of DR, can be used as a firststep screening tool in DR screening with considerable effectiveness.

= 7, the highest SNR is obtained for the mem, rms, and geometric mean operators, while the lowest SNR is obtained for the maximum operator. When comparing SNR for adipose and fibrous tissue, the level is close to 1.91 for adipose tissue but only 1.7 for fibrous tissue which contain relatively few organized scattering structures.

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