Assessing the importance of features for detection of hard exudates in retinal images
Küçük Resim Yok
Tarih
2017
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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 classi er (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 classi er algorithms\\' performances by using 5-fold cross validation on these important features\\' dataset and it was observed that the random forest (RF) classi er is the best classi er. 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 classi er.
Açıklama
Anahtar Kelimeler
Mühendislik, Elektrik ve Elektronik
Kaynak
Turkish Journal of Electrical Engineering and Computer Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
25
Sayı
2
