Automated detection of hard exudates in retinal fundus images for diabetic retinopathy screening using textural-based radon transform and morphology reconstruction

Background

Diabetic retinopathy (DR) screening requires accurate detection of hard exudates (HEs) in retinal images. This study presents a novel method that integrates textural-based Radon transform (RT) with morphological image processing techniques to automate the detection and segmentation of HEs in color fundus photography (CFP) images. By enhancing the diagnostic capabilities for DR, this approach aims to provide ophthalmologists with a reliable and efficient tool for identifying early signs of vascular damage associated with diabetes.

Method

The proposed algorithm was evaluated on two datasets, DIARETDB1 (89 images) and MUMS-DB (32 images). We developed an automated method for detecting HEs in CFP images. The approach involves a comprehensive framework comprising preprocessing, main processing, feature extraction, and post-processing. Key techniques include Radon transform for optical disc, vessels and soft and hard exudate feature extraction, and morphological reconstruction for enhancing detection accuracy. We employed Kirsch edge detection to distinguish HEs based on edge sharpness and utilized Top-Hat transformation to highlight small-scale features. The method integrates clinical expertise with computational techniques to differentiate between morphologically similar lesions. Performance was assessed through classification and pixel-based classification. metrics.

Results

The proposed algorithm demonstrated high performance in pixel-based classification, achieving best sensitivity of 92 %, and specificity 100 %. In lesion-based classification, the model achieved 100 % sensitivity and 100 % specificity on MUMS-DB datasets in the best case.

Conclusion

This integrated methodology successfully addresses the challenging task of differentiating between morphologically similar lesions, representing a significant advancement in automated DR screening. While performance varied between datasets, the results demonstrate strong potential for clinical application.