Classification and detection of diabetic retinopathy

Journal of emerging technologies and innovative research, 2021

Today diabetes is the most common disease faced by world population. High glucose level is present in the diabetes patients. Excessive glucose level causes damage in retinal vasculature. This damage ultimately results in Diabetic Retinopathy. DR is major reason of blindness in middle age population. Lesions detection from retinal images can provide automatic DR detection tool. Any sophisticated image classifier can give higher accuracy in automatic DR classification if inputted with appropriate features for training and classification. These appropriate features can be extracted from original retinal image along with Blood Vessel Segmented, Optic Disc Segmented and Microaneurysm detected images. This paper presents idea of Blood Vessel Segmentation, Optic Disc Segmentation and Microaneurysm detection using adaptive thresholding, binarization and wavelet model respectively. If these segmented images along with original retinal image are used for feature extraction then it can produce great accuracy in automatic DR classification.

Automated detection of lesions in the retinal images is an important step, in detection of diabetic retinopathy. The appearance and location of these lesions such as microaneurysms, hemorrhages, exudates and cotton wool spots are used for the assessment of diabetic retinopathy. The early sign of diabetic retinopathy is recognition of microaneurysm. The detection of lesions helps ophthalmologist to detect the emergence of its early symptoms and determine the next action step for the patient. A number of studies have been investigated on retinal fundus images for diabetic retinopathy. Although many algorithms have been developed, the accurate detection of lesions in retinal images is still a challenging problem. Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. In this paper a local adaptive thresholding method to detect and identify red lesions in retinal images is introduced. This technique is tested on publicly available DIARETDBI database.

Integrated Intelligent Research, 2012

Glaucoma is an eye disease that can result in blindness if it is not detected and treated in proper time. Diabetic related eye diseases like Diabetic Retinopathy (DR), Diabetic Maculopathy (DM) are major cause of blindness. Early detection of diabetic diseases plays an important role to prevent blindness. In last few years there are several researches done in medical image processing and detection from the fund us images such as Optic disk (OD) and the retinal vessels done in the automated detection of Diabetic retinopathy (DR), Diabetic Maculopathy (DM). This research paper represents the methods which are used in the automated detection of diabetic retinopathy. The recent methods used to detect the factors like hemorrhages and Micro Aneurysms (MA) are also discussed in this paper.

—Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy.

Multimedia Tools and Applications, 2020

Diabetic retinopathy (DR) is a leading cause of preventable blindness caused by damaged blood vessels in the eye, if not treated early on. The aim of this research work was to develop a method for the automatic detection of Diabetic Retinopathy and proposing a model for deciding the progression/severity using fundus images. The method was developed so that DR can be detected in an effective and efficient manner before causing damage to the eye, without the presence of an ophthalmologist. The manual screening requires the presence of an ophthalmologist and the resource of time. Detecting exudates is important for the diagnosis of DR. The approach adopted was twofold: i. extracting features of interest from the images i.e. the blood vessels, optic disc (OD), exudates and microaneurysms by using morphological operations and ii. classifying its progression/ severity as either mild or moderate by using the support vector machine (SVM) classifier for helping Ophthalmologists. The performance of the proposed method has been assessed by an ophthalmologist and approved. This paper contributes towards the field of automatic detection of anomalous structures and their severity.

Proceedings - 2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011, 2011

Diabetes occurs when the pancreas fails to secrete enough insulin, slowly affecting the retina of the human eye. As it progresses, the vision of a patient starts deteriorating, leading to diabetic retinopathy. In this regard, retinal images acquired through fundal camera aid in analyzing the consequences, nature, and status of the effect of diabetes on the eye. The objectives of this study are to (i) detect blood vessel, (ii) identify hemorrhages and (iii) classify different stages of diabetic retinopathy into normal, moderate and nonproliferative diabetic retinopathy (NPDR). The basis of the classification of different stages of diabetic retinopathy is the detection and quantification of blood vessels and hemorrhages present in the retinal image. Retinal vascular is segmented utilising the contrast between the blood vessels and surrounding background. Hemorrhage candidates were detected using density analysis and bounding box techniques. Finally, classification of the different stages of eye disease was done using Random Forests technique based on the area and perimeter of the blood vessels and hemorrhages. Accuracy assessment of the classified output revealed that normal cases were classified with 90% accuracy while moderate and severe NPDR cases were 87.5% accurate.

According to the increasing consumption of sugar materials in human life and growing trend of the machine life, the prevalence of diabetes is on the rise. It is observed all patients with this disease mostly suffer from decrease or loss their vision. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. The retinal fundus images of the patients are procured by capturing the fundus of the eye with a digital fundus camera. The abnormalities in retinal fundus images due to DR are lesions which includes Microaneurysms and Haemorrhages. The proposed method is to detect the abnormalities in retinal fundus image is based on dynamic shape features and SVM classifier. In this study the Gaussian filter is used to enhance images and separate vessels with a high brightness intensity distribution. In this study canny's edge detector is used for detection of Lesions and features are extracted. SVM is used for classification of DR as NPDR or PDR.

3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the, 2003

Purpose: The purpose is to develop an automatic computerized screening system to recognize automatically the main components of the retina, an important features of background diabetic retinopathy and classify the normal, abnormal and unknown retinal image. Methods: This paper has presented 4 main methods to succeed of retinal diagnosis. Firstly, the retinal images are pre-processed via adaptive, local, contrast enhancement Secondly, the main features of a retinal image were defined as the optic disc, and blood vessels. The optic discs were located by identifying the area with the highest variation in intensity of adjacent pixels. Blood vessels were identified by means of a multilayer perceptron neural network, for which the inputs were derived from a principal component analysis of the image and edge detection of the intensity. Next, the background diabetic retinopathy features are identified. Recursive region growing segmentation algorithms were applied to detect the hard exudates. The haemorrhages and microaneurysms were recognised by detecting all feature similar to the blood vessels and removed the vessels out. Finally, all information is accumulated and diagnosed for diabetic retinopathy or a normal retina. Result: The diabetic retinopathy screening technique has been applied to the 484 normal retina images and 283 images with diabetic retinopathy. The sensitivity and specificity for the computerized screening program to classify the images were corrected 80.21% and 70.66% respectively. Conclusion: The computerized screening system has been developed to classify the normal and abnormalities of retinal images. The development of getting higher performance is in progress.

Journal of the Optical Society of America. A, Optics, image science, and vision, 2016

We develop an automated image processing system for detecting microaneurysm (MA) in diabetic patients. Diabetic retinopathy is one of the main causes of preventable blindness in working age diabetic people with the presence of an MA being one of the first signs. We transform the eye fundus images to the L*a*b* color space in order to separately process the L* and a* channels, looking for MAs in each of them. We then fuse the results, and last send the MA candidates to a k-nearest neighbors classifier for final assessment. The performance of the method, measured against 50 images with an ophthalmologist's hand-drawn ground-truth, shows high sensitivity (100%) and accuracy (84%), and running times around 10 s. This kind of automatic image processing application is important in order to reduce the burden on the public health system associated with the diagnosis of diabetic retinopathy given the high number of potential patients that need periodic screening.

Computers in biology and medicine, 2017

This paper presents a methodology and first results of an automatic detection system of first signs of Diabetic Retinopathy (DR) in fundus images, developed for the Health Ministry of the Andalusian Regional Government (Spain). The system detects the presence of microaneurysms and haemorrhages in retinography by means of techniques of digital image processing and supervised classification. Evaluation was conducted on 1058 images of 529 diabetic patients at risk of presenting evidence of DR (an image of each eye is provided). To this end, a ground-truth diagnosis was created based on gradations performed by 3 independent ophthalmology specialists. The comparison between the diagnosis provided by the system and the reference clinical diagnosis shows that the system can work at a level of sensitivity that is similar to that achieved by experts (0.9380 sensitivity per patient against 0.9416 sensitivity of several specialists). False negatives have proven to be mild cases. Moreover, whil...