Exudates Detection in Retinal Images Using KNNFP and WKNNFP Classifiers

Exudates are one of the primary signs of diabetic retinopathy, which is the main cause of blindness and can be prevented with an early screening process. In this paper, KNNFP and WKNNFP classifiers have been used for automatic exudates detection. The publicly available diabetic retinopathy dataset DIARETDB1 has been used in the evaluation process. The RGB image is converted to HIS color space. The median filter is applied to intensity image of HIS for removal of noise followed by Contrast-Limited Adaptive Histogram Equalization to achieve uniform illumination. Further the optic disk is eliminated since optic disk has properties similar to exudates,which may cause the hindrance with exudates detection. Five pixel level features were selected as input for classfication of exudates and non-exudates pixels: hue from hue image and intensity, mean intensity, standard deviation of intensity and distance between mean of optic disk pixels and pixels of exudates and non-exudates extracted from the preprocessed Intensity image. KNNFP and WKNNFP classifiers have been experimented using two distance measures namely Euclidean distance and Manhattan distance. Investigation reveals that the performance of KNNFP using Euclidean distance is superior when compared to KNNFP using Manhattan distance. WKKNFP has been experimented using three attribute weight assignment methods:Relief, information gain and Gain ratio.Compared to KNNFP, there is substantial improvement of WKKFP performance by assigning the feature weight Gain ratio and Relief method. The classfication accuracy of WKNNFP is found to be 97.50% compared to classfication accuracy of 96.67% with KNNFP classifier.