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 Detection and Classification of Early Stage Lesions in Diabetic Retinopathy using Color Fundus Images

2019 ◽  
Vol 8 (3) ◽  
pp. 4476-4480
Keyword(s):  
Diabetic Retinopathy ◽  
Clustering Algorithm ◽  
Early Stage ◽  
High Sensitivity ◽  
Morphological Operations ◽  
Hybrid Features ◽  
Speeded Up Robust Features ◽  
Fuzzy C Means Clustering ◽  
Color Fundus Images

Detection of lesions and classification of Diabetic Retinopathy (DR) play an important role in day-to-day life. In this proposed system, colour fundus image is pre-processed using morphological operations to recover from noises and it is converted into HSV colorspace. Fuzzy C-Means Clustering algorithm (FCMC) is used for segmenting the early stage lesions such as Microaneurysms (Ma), Haemorrhages (HE) and Exudates. Hybrid features such as colour correlogram and speeded up robust features (surf) are extracted to train the classifier. Cascaded Rotation Forest (CRF) classifier is used for classification of diabetic retinopathy. The proposed system increases the accuracy of detection and it has got high sensitivity.

scholarly journals Entropy Rate Superpixel Classification for Automatic Red Lesion Detection in Fundus Images

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 417 ◽  
Author(s):  
Roberto Romero-Oraá ◽  
Jorge Jiménez-García ◽  
María García ◽  
María I. López-Gálvez ◽  
Javier Oraá-Pérez ◽  
...  
Keyword(s):  
Large Scale ◽  
Developed Countries ◽  
Lesion Detection ◽  
Entropy Rate ◽  
Diabetic Patients ◽  
Fundus Images ◽  
Test Set ◽  
Image Appearance ◽  
Working Age ◽  
Color Fundus Images

Diabetic retinopathy (DR) is the main cause of blindness in the working-age population in developed countries. Digital color fundus images can be analyzed to detect lesions for large-scale screening. Thereby, automated systems can be helpful in the diagnosis of this disease. The aim of this study was to develop a method to automatically detect red lesions (RLs) in retinal images, including hemorrhages and microaneurysms. These signs are the earliest indicators of DR. Firstly, we performed a novel preprocessing stage to normalize the inter-image and intra-image appearance and enhance the retinal structures. Secondly, the Entropy Rate Superpixel method was used to segment the potential RL candidates. Then, we reduced superpixel candidates by combining inaccurately fragmented regions within structures. Finally, we classified the superpixels using a multilayer perceptron neural network. The used database contained 564 fundus images. The DB was randomly divided into a training set and a test set. Results on the test set were measured using two different criteria. With a pixel-based criterion, we obtained a sensitivity of 81.43% and a positive predictive value of 86.59%. Using an image-based criterion, we reached 84.04% sensitivity, 85.00% specificity and 84.45% accuracy. The algorithm was also evaluated on the DiaretDB1 database. The proposed method could help specialists in the detection of RLs in diabetic patients.

Image Processing for the Detection of Diabetic Retinopathy and Diabetic Macular Edema

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
R. Manjula Sri ◽  
K.M. M. Rao
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Blood Vessels ◽  
Diabetic Macular Edema ◽  
Severity Index ◽  
Diabetic Patients ◽  
Color Analysis ◽  
Hard Exudates ◽  
Patients With Diabetes

Diabetic retinopathy (DR) and diabetic macular edema (DME) are common microvascular retinal diseases in patients with diabetes. The diabetic patients may have a sudden and devastating impact on visual acuity, in the long run leading to blindness. Advanced stages of DR are characterized by the growth of abnormal retinal blood vessels secondary to ischemia. These blood vessels grow in an attempt to supply oxygenated blood to the hypoxic retina. At any time during the progression of DR, patients with diabetes can also develop DME, which involves retinal thickening in the macular area. In the present paper, algorithms are developed to detect DR and DME. For detecting DR the abnormalities in the retina blood vessels are detected by classifying the common abnormalities namely microaneurisms, hard exudates, heammorages and cotton wool spots. DME is detected by finding the nearness of Hard exudate to macula. The macula and hard exudates are localized using image processing techniques. Severity of DME is assessed based on the nearest exudates, their area and color analysis. The algorithm is tested with 65 DR and DME images with severity index 0, 1 and 2 from MESSIDOR database.

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.

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