Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation.

Dingchang Zheng,Shengchun Long,Shahina Pardhan,Zhiqing Chen,Xiaoxiao Huang

doi:10.1155/2019/3926930

Abstract

Diabetic retinopathy (DR) is one of the most common causes of visual impairment. Automatic detection of hard exudates (HE) from retinal photographs is an important step for detection of DR. However, most of existing algorithms for HE detection are complex and inefficient. We have developed and evaluated an automatic retinal image processing algorithm for HE detection using dynamic threshold and fuzzy C-means clustering (FCM) followed by support vector machine (SVM) for classification. The proposed algorithm consisted of four main stages: (i) imaging preprocessing; (ii) localization of optic disc (OD); (iii) determination of candidate HE using dynamic threshold in combination with global threshold based on FCM; and (iv) extraction of eight texture features from the candidate HE region, which were then fed into an SVM classifier for automatic HE classification. The proposed algorithm was trained and cross-validated (10 fold) on a publicly available e-ophtha EX database (47 images) on pixel-level, achieving the overall average sensitivity, PPV, and F-score of 76.5%, 82.7%, and 76.7%. It was tested on another independent DIARETDB1 database (89 images) with the overall average sensitivity, specificity, and accuracy of 97.5%, 97.8%, and 97.7%, respectively. In summary, the satisfactory evaluation results on both retinal imaging databases demonstrated the effectiveness of our proposed algorithm for automatic HE detection, by using dynamic threshold and FCM followed by an SVM for classification.

Highlights

Diabetic retinopathy (DR) is one of the major complications of diabetes that can lead to vision loss
We have developed and evaluated an automatic retinal image processing algorithm to detect hard exudates (HE) using dynamic threshold, fuzzy C-means clustering (FCM) and support vector machine (SVM)
The color retinal images were segmented using dynamic threshold in combination with the global threshold, and the segmented regions were classified into two disjoint classes using SVM

Summary

Introduction

Diabetic retinopathy (DR) is one of the major complications of diabetes that can lead to vision loss. Regular screening to detect retinopathy can potentially reduce the risk of blindness of patients. It is known that the occurrence of hard exudates (HE) is one of the main threats to vision loss especially when they occur near or on fovea [3]. HE appears at late background and NPDR stages on the surface of retina as bright yellowish or white at different locations [4] and with variable shapes and sizes ranging from a few pixels to thousands of pixels in the retinal images. It is well accepted that the detection of HE in color retinal images plays a vital role in DR diagnosis and monitoring the progress of treatment. HE detection is the main emphasis of this study

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