Assessing the importance of features for detection of hard exudates in retinal images

Abstract

Diabetes disrupts the operation of the eye and leads to vision loss, affecting particularly the nerve layer and capillary vessels in this layer by changes in the blood vessels of the retina. Suddenly loss and blurred vision problems occur in the image, depending on the phase of the disease, called diabetic retinopathy. Hard exudates are one of the primary signs of diabetic retinopathy. Automatic recognition of hard exudates in retinal images can contribute to detection of the disease. We present an automatic screening system for the detection of hard exudates. This system consists of two main steps. Firstly, the features were extracted from patch images consisting of hard exudate and normal regions using the DAISY algorithm based on the histogram of oriented gradients. After, we utilized the recursive feature elimination (RFE) method, using logistic regression (LR) and support vector classifier (SVC) estimators on the raw dataset. Therefore, we obtained two datasets containing the most important features. The number of important features in each dataset created with LR and SVC was 126 and 259, respectively. Afterward, we observed different classifier algorithms' performances by using 5-fold cross validation on these important features' dataset and it was observed that the random forest (RF) classifier is the best classifier. Secondly, we obtained important features from the feature vector that corresponds with the region of interest in accordance with the keypoint information in a new retinal fundus image. Then we performed detection of hard exudate regions on the retinal fundus image by using the RF classifier.

Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

“…Artificial neural networks can identify hemorrhages, microaneurysms, hard exudates, and cotton wool spots [72]. Exudates can also be identified by Fisher's linear discriminant analysis and recursive elimination using logistic regression [73,74]. The main limitation as part of exudate detection is the small area it occupies and the similar structure of the optic disc.…”

The computer based method to diabetic retinopathy assessment in retinal images: a review

“…After extracting the hard exudate location by DAISY algorithm, different methods and classifier were used to categorize the hard exudate on raw dataset. Eventually, after analyzing the data, random forest classifier is detected as the best classifier (38). Naqvi SA et al presented an automated system for extraction of soft exudates as cotton wool spots (CWS).…”

GMACA ile Hareket Tespiti Yapılan Video Görüntülerde İnsan Hareketlerinin Tanınması

“…HOG tanımlayıcılarının arkasındaki temel fikir, bir görüntü içindeki yerel nesne görünümünün ve şeklinin yoğunluk eğilimlerinin veya kenar yönlerinin dağılımı ile tanımlanabilmesidir. HOG bilgisayarla görü ve görüntü işlemede sıkça kullanılmaktadır [31,32].…”