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

Kemal AKYOL; Baha ŞEN; Şafak BAYIR; Hasan Basri ÇAKMAK

Makale detayları

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

Yazarlar:

Kemal AKYOL | Baha ŞEN | Şafak BAYIR | Hasan Basri ÇAKMAK

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 2, pp.1223-1237

Belge Tipi:

Makale

Yayın Tarihi:

3/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000741083

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

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 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.

Makale detayları

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

Yazarlar: Kemal AKYOL | Baha ŞEN | Şafak BAYIR | Hasan Basri ÇAKMAK

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 2, pp.1223-1237

Belge Tipi: Makale

Yayın Tarihi: 3/2017

Belge Dili: Türkçe

Document ID (Org No.): 2000741083

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Kemal AKYOL | Baha ŞEN | Şafak BAYIR | Hasan Basri ÇAKMAK

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 2, pp.1223-1237

Belge Tipi:

Makale

Yayın Tarihi:

3/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000741083

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632