Feature extraction from retinal fundus image for early detection of diabetic retinopathy

2013 ◽  
Author(s):  
Syna Sreng ◽  
Jun-Ichi Takada ◽  
Noppadol Maneerat ◽  
Don Isarakorn ◽  
Ruttikorn Varakulsiripunth ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis

2016 ◽  
Vol 10 (2) ◽  
pp. 254-261 ◽  
Author(s):  
Malavika Bhaskaranand ◽  
Chaithanya Ramachandra ◽  
Sandeep Bhat ◽  
Jorge Cuadros ◽  
Muneeswar Gupta Nittala ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Fundus Image ◽  
Diabetic Retinopathy Screening ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Retinopathy Screening

scholarly journals A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

2014 ◽  
Vol 14 (3) ◽  
pp. 5494-5499
Author(s):  
Sreeparna Banerjee ◽  
Diptoneel Kayal
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Visual Inspection ◽  
Fundus Image ◽  
Analysis Program ◽  
Hard Exudates ◽  
Lighting Conditions ◽  
Retinal Fundus Image ◽  
Inner Diameter ◽  
Retinal Fundus

Diabetic retinopathy is considered to be one of the major causes of blindness among diabetes mellitus patients. Due to diabetic retinopathy blood vessels of retina gets damaged and fat, lipoprotein substances gets leaked out of the damaged blood vessels and are deposited in the intra retinal space. These substances are viewed as yellowish or whitish in color and are termed as exudates. They are the most important visible sign of the presence of diabetic retinopathy. Exudates are of two types, (a) hard exudates and (b) soft exudates. If the disease is not detected in early stages then it may lead to complete loss of vision to the diabetes patients. Detection of exudates is extremely difficult to detect by visual inspection due to small inner diameter of retina and inadequate lighting conditions. An efficient image analysis program can detect the presence effectively. In this paper we have proposed an automatic method for detection of hard exudates. The proposed method exhibits a sensitivity of 97.60% and specificity of 93% and accuracy of 95.70%.

scholarly journals Diabetic Retinopathy Detection using Image Processing

2019 ◽  
Vol 8 (6S) ◽  
pp. 937-941
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Medical Image ◽  
Histogram Equalization ◽  
Fundus Image ◽  
Adaptive Histogram Equalization ◽  
Retinal Fundus Image ◽  
Equalization Algorithm ◽  
Processing Techniques ◽  
Retinal Fundus

The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB (R2010a) and it is widely used in image processing. This paper proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with open CV (Computer Vision) framework implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and 98% of Accuracy can be achieved in the detection of DR.

Diabetic Retinopathy Prediction Device System

2019 ◽  
Vol 16 (10) ◽  
pp. 4266-4270
Author(s):  
Meenu Garg ◽  
Sheifali Gupta ◽  
Rakesh Ahuja ◽  
Deepali Gupta
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Treatment Effect ◽  
Physiological Parameters ◽  
Early Prediction ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Image Capturing ◽  
The One ◽  
Retinal Fundus

The present study relates to diagnostic devices, and more specifically, to a diabetic retinopathy prediction device, system and method for early prediction of diabetic retinopathy with application of deep learning. The device includes an image capturing device, a memory coupled to processor. The image capturing device obtains a retinal fundus image from the user. The memory comprising executable instructions which upon execution by the processor configures the device to obtain physiological parameters of the user in real-time from the image capturing device, retrieve the obtained retinal fundus image and the one or more obtained physiological parameters and compare the one or more extracted features with at least one pre-stored feature in a database to generate at least a prediction result indicative of detection of the presence, the progression or the treatment effect of the disease in the user.

Texture Based Feature Extraction and Classification of Retinal Fundus Image for Glaucoma Detection

2021 ◽  
Author(s):  
E. Anna Devi ◽  
Abdul Wahid Nasir ◽  
E. Ahila Devi ◽  
N. Mahesh ◽  
Pavithra G ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fundus Image ◽  
Glaucoma Detection ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Pengaruh Preprocessing Terhadap Klasifikasi Diabetic Retinopathy dengan Pendekatan Transfer Learning Convolutional Neural Network

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Juan Elisha Widyaya ◽  
Setia Budi
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Careful Examination ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is eye diseases caused by diabetic mellitus or sugar diseases. If DR is detected in early stage, the blindness that follow can be prevented. Ophthalmologist or eye clinician usually decide the stage of DR from retinal fundus images. Careful examination of retinal fundus images is time consuming task and require experienced clinicians or ophthalmologist but a computer which has been trained to recognize the DR stages can diagnose and give result in real-time manner. One approach of algorithm to train a computer to recognize an image is deep learning Convolutional Neural Network (CNN). CNN allows a computer to learn the features of an image, in our case is retinal fundus image, automatically. Preprocessing is usually done before a CNN model is trained. In this study, four preprocessing were carried out. Of the four preprocessing tested, preprocessing with CLAHE and unsharp masking on the green channel of the retinal fundus image give the best results with an accuracy of 79.79%, 82.97% precision, 74.64% recall, and 95.81% AUC. The CNN architecture used is Inception v3.

scholarly journals Retinal Fundus Image Research for Diagnosis of Diabetic Maculopathy

2019 ◽  
Vol 8 (2S11) ◽  
pp. 339-347
Keyword(s):  
Feature Extraction ◽  
Texture Features ◽  
Input Image ◽  
Image Features ◽  
Support Vector ◽  
Fundus Image ◽  
Diabetic Maculopathy ◽  
Retinal Fundus Image ◽  
Sight Loss ◽  
Retinal Fundus

Fundus images are valuable resources in diagnosis of retinal diseases. This paper proposes a computer-aided method based on various feature extraction techniques and support vector machines (SVM) for detection and classification of diabetic maculopathy (DM). DM, defined as retinopathy within one disc diameter of the centre of the macula, is a major cause of sight loss in diabetes. Here, we bring out a new approach to detect DM based on retinal fundus image features. During the first stage the input image is enhanced and the optic disc is masked to determine the presence of regions of foveal neighborhood. The second stage, deals with various feature extraction technique based on transform, shape and texture features. Extracted features are further categorized as healthy or affected images. Here we go for classification task using the RBF Support Vector Machine (SVM) classification, the techniques have been tested on retinal databases and these are compared with trained phase to categorize Healthy and DM images. This method can detect DM with a level accuracy on par with human retinal specialists

Sign in / Sign up

Export Citation Format

Share Document