scholarly journals Automated Detection and Counting of Hard Exudates for Diabetic Retinopathy by using Watershed and Double Top-Bottom Hat Filtering Algorithm

2021 ◽  
Vol 5 (3) ◽  
pp. 242
Author(s):  
Dafwen Toresa ◽  
Mohamad Azrul Edzwan Shahril ◽  
Nor Hazlyna Harun ◽  
Juhaida Abu Bakar ◽  
Hidra Amnur
Keyword(s):  
Diabetic Retinopathy ◽  
Imaging System ◽  
System Development ◽  
Ground Truth ◽  
Automated Detection ◽  
Retinal Images ◽  
Segmentation Method ◽  
Hard Exudates ◽  
Detection And Counting

Diabetic Retinopathy (DR) is one of diabetes complications that affects our eyes. Hard Exudate (HE) are known to be the early signs of DR that potentially lead to blindness. Detection of DR automatically is a complicated job since the size of HE is very small. Besides, our community nowadays lack awareness on diabetic where they do not know that diabetes can affect eyes and lead to blindness if regular check-up is not performed. Hence, automated detection of HE known as Eye Retinal Imaging System (EyRis) was created to focus on detecting the HE based on fundus image. The purpose of this system development is for early detection of the symptoms based on retina images captured using fundus camera. Through the captured retina image, we can clearly detect the symptoms that lead to DR. In this study, proposed Watershed segmentation method for detecting HE in fundus images. Top-Hat and Bottom-Hat were use as enhancement technique to improve the quality of the image. This method was tested on 15 retinal images from the Universiti Sains Malaysia Hospital (HUSM) at three different stages: Normal, NPDR, and PDR. Ten of these images have abnormalities, while the rest are normal retinal images. The evaluation of the segmentation images would be compared by Sensitivity, F-score and accuracy based on medical expert's hand drawn ground truth. The results achieve accuracy 0.96 percent with 0.99 percent sensitivity for retinal images.

scholarly journals EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Cheng Wan ◽  
Yingsi Chen ◽  
Han Li ◽  
Bo Zheng ◽  
Nan Chen ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Ground Truth ◽  
Clinical Analysis ◽  
Segmentation Method ◽  
Hard Exudates ◽  
Percent Improvement ◽  
Detection And Counting ◽  
Novel Method ◽  
Matching Degree ◽  
Precision Recall Curve

Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and counting of them are a basic but important work. The manual annotation of these lesions is a labor-intensive task in clinical analysis. To solve the problem, we proposed a novel segmentation method for different lesions in DR. Our method is based on a convolutional neural network and can be divided into encoder module, attention module, and decoder module, so we refer it as EAD-Net. After normalization and augmentation, the fundus images were sent to the EAD-Net for automated feature extraction and pixel-wise label prediction. Given the evaluation metrics based on the matching degree between detected candidates and ground truth lesions, our method achieved sensitivity of 92.77%, specificity of 99.98%, and accuracy of 99.97% on the e_ophtha_EX dataset and comparable AUPR (Area under Precision-Recall curve) scores on IDRiD dataset. Moreover, the results on the local dataset also show that our EAD-Net has better performance than original U-net in most metrics, especially in the sensitivity and F1-score, with nearly ten percent improvement. The proposed EAD-Net is a novel method based on clinical DR diagnosis. It has satisfactory results on the segmentation of four different kinds of lesions. These effective segmentations have important clinical significance in the monitoring and diagnosis of DR.

An efficient multistage segmentation method for accurate hard exudates and lesion detection in digital retinal images

2017 ◽  
Vol 33 (3) ◽  
pp. 1639-1649 ◽  
Author(s):  
Karim Adinehvand ◽  
Dariush Sardari ◽  
Mohammad Hosntalab ◽  
Majid Pouladian
Keyword(s):  
Lesion Detection ◽  
Retinal Images ◽  
Segmentation Method ◽  
Hard Exudates

scholarly journals Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

2020 ◽  
Vol 8 (1) ◽  
pp. e000892 ◽  
Author(s):  
Bhavana Sosale ◽  
Sosale Ramachandra Aravind ◽  
Hemanth Murthy ◽  
Srikanth Narayana ◽  
Usha Sharma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Smart Phone ◽  
High Sensitivity ◽  
Ground Truth ◽  
Cross Sectional Study ◽  
Retinal Images ◽  
Cross Sectional ◽  
Smart Study

IntroductionThe aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.MethodsThis cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).ResultsAnalysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3% (95% CI 80.9% to 85.7%) and 95.5% (95% CI 94.1% to 96.8%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93% (95% CI 91.3% to 94.7%) and 92.5% (95% CI 90.8% to 94.2%).ConclusionThe Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.

scholarly journals Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network

2018 ◽  
Vol 11 (3) ◽  
pp. 1083
Author(s):  
Jeyapriya J ◽  
K S Umadevi ◽  
R Jagadeesh Kannan
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Automated Detection ◽  
Convolution Neural Network ◽  
Retinal Images ◽  
Fundus Images ◽  
Catching Up

The diagnosing features for Diabetic Retinopathy (DR) comprises of features occurring in and around the regions of blood vessel zone which will result into exudes, hemorrhages, microaneurysms and generation of textures on the albumen region of eye balls. In this study we presenta probabilistic convolution neural network based algorithms, utilized for the extraction of such features from the retinal images of patient’s eyeballs. The classifications proficiency of various DR systems is tabulated and examined. The majority of the reported systems are profoundly advanced regarding the analyzed fundus images is catching up to the human ophthalmologist’s characterization capacities.

Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy

2012 ◽  
Vol 51 (20) ◽  
pp. 4858 ◽  
Author(s):  
M. Usman Akram ◽  
Anam Tariq ◽  
M. Almas Anjum ◽  
M. Younus Javed
Keyword(s):  
Diabetic Retinopathy ◽  
Automated Detection ◽  
Retinal Images

scholarly journals Automated Detection of Diabetic Retinopathy for Early Diagnosis using Exudate Images

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1572-1576
Keyword(s):  
Diabetic Retinopathy ◽  
Early Diagnosis ◽  
Sensitivity And Specificity ◽  
Automatic Recognition ◽  
Automated Detection ◽  
Retinal Images ◽  
Feature Classification ◽  
Detection Process ◽  
Performance Of Algorithm ◽  
Retinal Exudates

Retina plays a vital character in detection of various diseases in early point such as diabetes retinopathy which can be performed by analyzing the retinal images [6]. Diseased patients have to undergo periodic screening of eye. Standouts amongst the most predominant clinical indications of diabetic retinopathy are exudates [17]. To detect diabetic retinopathy in patients the ophthalmologist inspects the exudates by Ophthalmoscopy [17] where recognition of exudates is a vital diagnostic undertaking in which computer help may assume a noteworthy job. But intrinsic characteristics of retinal images detection process is difficult for the ophthalmologists. Here, we proposed another algorithm “Superpixel Multi-Feature Classification" for the programmed automatic recognition of retinal exudates successfully and to encourage ophthalmologist to give better patient finding experiencing diabetic retinopathy, advising them the level of seriousness ahead of time. The performance of algorithm has been compared as a result, the outcomes are effective and the sensitivity and specificity for our exudates identification is 80% and 91.28%, respectively [15].

scholarly journals Automated detection of diabetic retinopathy in retinal images

2016 ◽  
Vol 64 (1) ◽  
pp. 26 ◽  
Author(s):  
Carmen Valverde ◽  
Maria Garcia ◽  
Roberto Hornero ◽  
MariaI Lopez-Galvez
Keyword(s):  
Diabetic Retinopathy ◽  
Automated Detection ◽  
Retinal Images

Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening

2004 ◽  
Vol 21 (1) ◽  
pp. 84-90 ◽  
Author(s):  
D. Usher ◽  
M. Dumskyj ◽  
M. Himaga ◽  
T. H. Williamson ◽  
S. Nussey ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Automated Detection ◽  
Retinal Images ◽  
Diabetic Retinopathy Screening ◽  
Retinopathy Screening
Sign in / Sign up

Export Citation Format

Share Document