Diabetic Retinal Fundus Images: Preprocessing and Feature Extraction for Early Detection of Diabetic Retinopathy
Dilip Singh Sisodia, Shruti Nair and Pooja KhobragadeNational Institute of Technology, Raipur.
Corresponding Author E-mail: dssisodia.cs@nitrr.ac.in
Abstract: The investigation of clinical reports suggested that more than ten percent patients with diabetes have a high risk of eye issues. Diabetic Retinopathy (DR) is an eye ailment which influences eighty to eighty-five percent of the patients who have diabetes for more than ten years. The retinal fundus images are commonly used for detection and analysis of diabetic retinopathy disease in clinics. The raw retinal fundus images are very hard to process by machine learning algorithms. In this paper, pre-processing of raw retinal fundus images are performed using extraction of green channel, histogram equalization, image enhancement and resizing techniques. Fourteen features are also extracted from pre-processed images for quantitative analysis. The experiments are performed using Kaggle Diabetic Retinopathy dataset, and the results are evaluated by considering the mean value and standard deviation for extracted features. The result yielded exudate area as the best-ranked feature with a mean difference of 1029.7. The result attributed due to its complete absence in normal diabetic images and its simultaneous presence in the three classes of diabetic retinopathy images namely mild, normal and severe.
Keywords: Diabetic retinopathy; image processing; retinal fundus images; feature extraction exudate area Back to TOC