Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME)

2019 ◽  
Vol 64 (3) ◽  
pp. 297-307 ◽  
Author(s):  
Muhammad Noor-ul-huda ◽  
Samabia Tehsin ◽  
Sairam Ahmed ◽  
Fuad A.K. Niazi ◽  
Zeerish Murtaza
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Medical Image ◽  
Vision Loss ◽  
Medical Image Analysis ◽  
Retinal Images ◽  
Diagnostic Systems ◽  
High Blood Glucose ◽  
Department Of Ophthalmology

Abstract Diabetes mellitus is an enduring disease related with significant morbidity and mortality. The main pathogenesis behind this disease is its numerous micro- and macrovascular complications. In developing countries, diabetic retinopathy (DR) is one of the major sources of vision impairment in working age population. DR has been classified into two categories: proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR). NPDR is further classified into mild, moderate and severe, while PDR is further classified into early PDR, high risk PDR and advanced diabetic eye disease. DR is a disease caused due to high blood glucose levels which result in vision loss or permanent blindness. High-level advancements in the field of bio-medical image processing have speeded up the automated process of disease diagnoses and analysis. Much research has been conducted and computerized systems have been designed to detect and analyze retinal diseases through image processing. Similarly, a number of algorithms have been designed to detect and grade DR by analyzing different symptoms including microaneurysms, soft exudates, hard exudates, cotton wool spots, fibrotic bands, neovascularization on disc (NVD), neovascularization elsewhere (NVE), hemorrhages and tractional bands. The visual examination of the retina is a vital test to diagnose DR-related complications. However, all the DR computer-aided diagnostic systems require a standard dataset for the estimation of their efficiency, performance and accuracy. This research presents a benchmark for the evaluation of computer-based DR diagnostic systems. The existing DR benchmarks are small in size and do not cover all the DR stages and categories. The dataset contains 1445 high-quality fundus photographs of retinal images, acquired over 2 years from the records of the patients who presented to the Department of Ophthalmology, Holy Family Hospital, Rawalpindi. This benchmark provides an evaluation platform for medical image analysis researchers. Furthermore, it provides evaluation data for all the stages of DR.

scholarly journals A Critical Insight into Pragmatic Manifestation of Diabetic Retinopathy Grading and Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 2040-2049
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Medical Image ◽  
Vision Loss ◽  
Medical Image Analysis ◽  
Research Trend ◽  
Automated Systems ◽  
First Choice

Nowadays, artificial intelligence applications invade all of the fields including medical applications field. Deep learning, a subfield of artificial intelligence, in particular, Convolutional Neural Networks (CNN), have quickly become the first choice for processing and analyzing medical images due to its performance and effectiveness. Diabetic retinopathy is a vision loss disease that infects people with diabetes. This disease damages the blood vessels in the retina, hence, leads to blindness. Due to the sensitivity and complications involved in managing diabetics, designing and developing automated systems to detect and grade diabetic retinopathy is considered one of the recent research areas in the world of medical image applications. In this paper, the aspects of deep learning field related to diabetic retinopathy have been discussed. Various concepts in deep learning including traditional Artificial Neural Network (ANN) algorithm, ANN drawbacks in context of computer vision and image processing applications, and the best algorithm to overcome ANN drawbacks, CNN, have been elucidated along with the architecture. The paper also reviews an extensive summary of some works in the current research trend and future applications of the DL algorithms in medical image analysis for DR detection and grading. Furthermore, various research gabs related to building such automated systems for medical image analysis have been conferred – such as imbalance dataset which is considered one of the main performance issues that should be handled, the need of high performance computational resources to train deep and efficient models and others. This is quite beneficial for researchers working in the domain of medical image analysis to handle DR.

scholarly journals Visual Cortex Transcranial Direct Current Stimulation for Proliferative Diabetic Retinopathy Patients: A Double-Blinded Randomized Exploratory Trial

2021 ◽  
Vol 11 (2) ◽  
pp. 270
Author(s):  
Angelito Braulio F. de Venecia ◽  
Shane M. Fresnoza
Keyword(s):  
Diabetic Retinopathy ◽  
Visual Cortex ◽  
Proliferative Diabetic Retinopathy ◽  
Direct Current ◽  
Transcranial Direct Current Stimulation ◽  
Vision Loss ◽  
Sham Stimulation ◽  
Direct Current Stimulation ◽  
Residual Vision ◽  
Cathodal Tdcs

Proliferative diabetic retinopathy (PDR) is a severe complication of diabetes. PDR-related retinal hemorrhages often lead to severe vision loss. The main goals of management are to prevent visual impairment progression and improve residual vision. We explored the potential of transcranial direct current stimulation (tDCS) to enhance residual vision. tDCS applied to the primary visual cortex (V1) may improve visual input processing from PDR patients’ retinas. Eleven PDR patients received cathodal tDCS stimulation of V1 (1 mA for 10 min), and another eleven patients received sham stimulation (1 mA for 30 s). Visual acuity (logarithm of the minimum angle of resolution (LogMAR) scores) and number acuity (reaction times (RTs) and accuracy rates (ARs)) were measured before and immediately after stimulation. The LogMAR scores and the RTs of patients who received cathodal tDCS decreased significantly after stimulation. Cathodal tDCS has no significant effect on ARs. There were no significant changes in the LogMAR scores, RTs, and ARs of PDR patients who received sham stimulation. The results are compatible with our proposal that neuronal noise aggravates impaired visual function in PDR. The therapeutic effect indicates the potential of tDCS as a safe and effective vision rehabilitation tool for PDR patients.

scholarly journals Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Tomi Kauppi ◽  
Joni-Kristian Kämäräinen ◽  
Lasse Lensu ◽  
Valentina Kalesnykiene ◽  
Iiris Sorri ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Medical Image ◽  
Image Annotation ◽  
Medical Image Analysis ◽  
Software Tool ◽  
Ground Truth ◽  
Spatial Span ◽  
Benchmark Database ◽  
Multiple Experts

We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medical images with verified ground truth and solid evaluation protocols. Such databases support the development of better algorithms, execution of profound method comparisons, and, consequently, technology transfer from research laboratories to clinical practice. For this purpose, we propose a framework consisting of reusable methods and tools for the laborious task of constructing a benchmark database. We provide a software tool for medical image annotation helping to collect class label, spatial span, and expert's confidence on lesions and a method to appropriately combine the manual segmentations from multiple experts. The tool and all necessary functionality for method evaluation are provided as public software packages. As a case study, we utilized the framework and tools to establish the DiaRetDB1 V2.1 database for benchmarking diabetic retinopathy detection algorithms. The database contains a set of retinal images, ground truth based on information from multiple experts, and a baseline algorithm for the detection of retinopathy lesions.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

scholarly journals Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Zhou ◽  
Chengdong Wu ◽  
Dali Chen ◽  
Zhenzhu Wang ◽  
Yugen Yi ◽  
...  
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Dictionary Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Critical Role ◽  
Medical Image Processing ◽  
Unified Framework ◽  
Semantic Relationships ◽  
Detection Approach

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

scholarly journals Retinal vessel feature extraction from fundus image using image processing techniques

2018 ◽  
Vol 7 (2) ◽  
pp. 687
Author(s):  
R. Lavanya ◽  
G. K. Rajini ◽  
G. Vidhya Sagar
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessels ◽  
Retinal Vessel ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Vessel Detection ◽  
Proper Diagnosis ◽  
Processing Techniques

Retinal Vessel detection for retinal images play crucial role in medical field for proper diagnosis and treatment of various diseases like diabetic retinopathy, hypertensive retinopathy etc. This paper deals with image processing techniques for automatic analysis of blood vessel detection of fundus retinal image using MATLAB tool. This approach uses intensity information and local phase based enhancement filter techniques and morphological operators to provide better accuracy.Objective: The effect of diabetes on the eye is called Diabetic Retinopathy. At the early stages of the disease, blood vessels in the retina become weakened and leak, forming small hemorrhages. As the disease progress, blood vessels may block, and sometimes leads to permanent vision loss. To help Clinicians in diagnosis of diabetic retinopathy in retinal images with an early detection of abnormalities with automated tools.Methods: Fundus photography is an imaging technology used to capture retinal images in diabetic patient through fundus camera. Adaptive Thresholding is used as pre-processing techniques to increase the contrast, and filters are applied to enhance the image quality. Morphological processing is used to detect the shape of blood vessels as they are nonlinear in nature.Results: Image features like, Mean and Standard deviation and entropy, for textural analysis of image with Gray Level Co-occurrence Matrix features like contrast and Energy are calculated for detected vessels.Conclusion: In diabetic patients eyes are affected severely compared to other organs. Early detection of vessel structure in retinal images with computer assisted tools may assist Clinicians for proper diagnosis and pathology. 

scholarly journals Wide-field imaging in proliferative diabetic retinopathy

2019 ◽  
Vol 5 (S1) ◽  
Author(s):  
T. Y. Alvin Liu ◽  
J. Fernando Arevalo
Keyword(s):  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Vision Loss ◽  
Current Data ◽  
Panretinal Photocoagulation ◽  
Main Body ◽  
Wide Field ◽  
Fundus Imaging ◽  
Field Imaging ◽  
Wide Field Imaging

Abstract Background Diabetic retinopathy (DR) is one of the leading causes of vision loss worldwide. For decades, 7-field 30-degree fundus imaging has been the gold standard for DR classification. The aim of this review article is to discuss how the advent of ultra-wide-field (UWF) fundus imaging has changed the management of proliferative diabetic retinopathy (PDR). Main body Current data suggests that UWF imaging, as compared to conventional Early Treatment Diabetic Retinopathy Study (ETDRS) fields, detects additional and more extensive PDR pathologies. DR lesions, captured by UWF imaging outside of ETDRS fields, likely carry prognostication value. Conclusion UWF imaging represents a major advancement in the detection and management of DR. It remains unclear whether, when and how patients, with PDR changes only peripheral to standard ETDRS fields, should be treated. A larger, prospective, randomized clinical trial is also needed to compare the efficacy of UWF image-guided targeted laser photocoagulation with that of conventional panretinal photocoagulation.

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26 ◽  
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

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