scholarly journals Detection of Hard Exudates in Color Fundus Images of the Human Retina

2012 ◽  
Vol 30 ◽  
pp. 297-302 ◽  
Author(s):  
C. JayaKumari ◽  
R. Maruthi
Keyword(s):  
Fundus Images ◽  
Human Retina ◽  
Hard Exudates ◽  
Color Fundus Images

NEW SEGMENTATION METHODOLOGY FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGES

2013 ◽  
Vol 13 (01) ◽  
pp. 1350014 ◽  
Author(s):  
A. FEROUI ◽  
M. MESSADI ◽  
I. HADJIDJ ◽  
A. BESSAID
Keyword(s):  
Macular Edema ◽  
Mathematical Morphology ◽  
Clustering Algorithm ◽  
High Sensitivity ◽  
Diabetic Patients ◽  
Fundus Images ◽  
Manual Segmentation ◽  
Hard Exudates ◽  
Color Fundus Images ◽  
Good Detection

In this work, we developed an approach based on mathematical morphology and the k-means clustering algorithm to detect hard exudates (HEs) in images taken by retinography from different diabetic patients. The presence of exudates within the macular region is a hallmark of diabetic macular edema and is detected by diagnostics with high sensitivity. In ophthalmologic images, the segmentation of HEs is essential to characterize the shape of the lesion for analysis. In this domain, several approaches have been employed for exudate extraction. Some authors have used only the mathematical morphology, but this approach does not provide very good detection of exudates. In this paper, we combined the k-means clustering algorithm and the mathematical morphology. This approach was tested on a set of 50 ophthalmologic images. The obtained results were compared with manual segmentation by an ophthalmologist.

scholarly journals Multi-label classification of fundus images based on graph convolutional network

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Yinlin Cheng ◽  
Mengnan Ma ◽  
Xingyu Li ◽  
Yi Zhou
Keyword(s):  
Large Scale ◽  
Fundus Images ◽  
Convolutional Network ◽  
Hard Exudates ◽  
Proposed Model ◽  
Diabetic Population ◽  
Color Fundus Images ◽  
Aided Diagnosis ◽  
Large Scale Screening

Abstract Background Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but the detection of multiple lesions is still a difficult point in current research. Methods This study proposed a multi-label classification method based on the graph convolutional network (GCN), so as to detect 8 types of fundus lesions in color fundus images. We collected 7459 fundus images (1887 left eyes, 1966 right eyes) from 2282 patients (1283 women, 999 men), and labeled 8 types of lesions, laser scars, drusen, cup disc ratio ($$C/D>0.6$$ C / D > 0.6 ), hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates. We constructed a specialized corpus of the related fundus lesions. A multi-label classification algorithm for fundus images was proposed based on the corpus, and the collected data were trained. Results The average overall F1 Score (OF1) and the average per-class F1 Score (CF1) of the model were 0.808 and 0.792 respectively. The area under the ROC curve (AUC) of our proposed model reached 0.986, 0.954, 0.946, 0.957, 0.952, 0.889, 0.937 and 0.926 for detecting laser scars, drusen, cup disc ratio, hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates, respectively. Conclusions Our results demonstrated that our proposed model can detect a variety of lesions in the color images of the fundus, which lays a foundation for assisting doctors in diagnosis and makes it possible to carry out rapid and efficient large-scale screening of fundus lesions.

Bin loss for hard exudates segmentation in fundus images

2020 ◽  
Vol 392 ◽  
pp. 314-324 ◽  
Author(s):  
Song Guo ◽  
Kai Wang ◽  
Hong Kang ◽  
Teng Liu ◽  
Yingqi Gao ◽  
...  
Keyword(s):  
Fundus Images ◽  
Hard Exudates
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