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[Communications in Computer and Information Science] Advances in Signal Processing and Intelligent Recognition Systems Volume 968 (4th International Symposium SIRS 2018, Bangalore, India, September 19–22, 2018, Revised Selected Papers) || A Comprehensive Review on Automatic Diagnosis of Diabetic Maculopathy in Retinal Fundus Images
摘要： Diabetic Maculopathy (DM) is one of the major problems of diabetes mellitus and it is one of the key reasons for the vision problem. It arises due to the leakage of blood from injured retinal veins. The development of DM is moderate and soundless and it is found in 10% of the world diabetic population. If diabetic maculopathy is not noticed in the underlying stage the effect this on macula is irreversible and can prompt vision loss. Therefore, screening of diabetic eye helps in finding diabetic maculopathy at the beginning stage which prevents the vision loss. In this review paper, the anatomy of the human eye and a brief overview of diabetes, diabetic retinopathy and diabetic maculopathy is presented. The literature review of various methods/techniques used for detection of DM in retinal fundus images and the performance metrics used to measure these methods are discussed in details. Issues involved in DM detection are also mentioned in this paper.
关键词： Blood vessels (BVs),Diabetic maculopathy (DM),Hard exudates (HEs),Retinopathy (DR),Optic disc (OD)
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Chennai (2018.3.22-2018.3.24)] 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) - Automatic Segmentation of Exudates in Retinal Images
摘要： This paper presents a new technique for segmentation of exudates in fundus images. This technique is based on Discrete Wavelet Transform (DWT) and histogram based thresholding procedure. In this work, Optic Disc (OD) is eliminated using DWT from original green component image prior segmentation of exudates. This step aids to avoid the misclassification of exudates region. Histogram based threshold calculation procedure is introduced for segmentation of bright regions in green component image. Hard exudates are obtained after masking the OD region in segmented bright regions of the green component image. This technique was evaluated on images from DIARETDB0 and DIARETDB1 databases. The average sensitivity, specificity and accuracy achieved by proposed method are 0.7890, 0.9972 and 0.9964 respectively. Comparison with existing methods offered in the literature shows that the performance of proposed approach is significant.
关键词： Optic Disc,Retinal image,Segmentation,Exudates,Discrete Wavelet Transform
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Soft-computing and Network Security (ICSNS) - Coimbatore, India (2018.2.14-2018.2.16)] 2018 International Conference on Soft-computing and Network Security (ICSNS) - A Survey for Diabetic Retinopathy
摘要： Diabetic retinopathy has ended up a exceptionally common eye illness which causes visual deficiency among individuals. This Study on diabetic retinopathy makes a great examination of how diverse strategy is they to identify. It may offer assistance to identify the malady as early as conceivable and allow them a conceivable treatment. Identifying the exudates in early organize can avoid a vision misfortune. Retinal blood vessel are recognized and utilized for them to detect. Due to the growing prevalence of metabolic disorders, ask for diabetic retinopathy (DR) screening stages is steeply growing. Early location and treatment of DR are key open wellbeing mediations that can significantly decrease the probability of vision misfortune. Current DR screening programs regularly utilize retinal fundus photography, which depends on gifted perusers for manual DR appraisal. Be that as it may, this is labor- intensive and endures from irregularity over destinations. Subsequently, there has been a later multiplication of robotized retinal picture investigation computer program that may potential
关键词： retina,Diabetic retinopathy,exudates,Image Segmentation
更新于2025-09-23 15:19:57
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Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images
摘要： Exudates is a serious complication causing blindness in diabetic retinopathy (DR) patients. The main objective of this study is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm (MMSA). The proposed methods start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using mean shift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied, in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy for of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.
关键词： retinal image,mathematical morphology,mean shift algorithm,Diabetic retinopathy,exudates
更新于2025-09-19 17:15:36
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An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images
摘要： Diabetic retinopathy (i.e., DR), is an eye disorder caused by diabetes, diabetic retinopathy detection is an important task in retinal fundus images due the early detection and treatment can potentially reduce the risk of blindness. Retinal fundus images play an important role through disease diagnosis, disease recognition (i.e., by ophthalmologists), and treatment. The current state-of-the-art techniques are not satisfied with sensitivity and specificity. In fact, there are still other issues to be resolved in state-of-the-art techniques such as performances, accuracy, and easily identify the DR disease effectively. Therefore, this paper proposes an effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images that will satisfy the performance metrics (i.e., sensitivity, specificity, accuracy). The proposed automatic screening system for diabetic retinopathy was conducted in several steps: Pre-processing, optic disc detection and removal, blood vessel segmentation and removal, elimination of fovea, feature extraction (i.e., Micro-aneurysm, retinal hemorrhage, and exudates), feature selection and classification. Finally, a software-based simulation using MATLAB was performed using DIARETDB1 dataset and the obtained results are validated by comparing with expert ophthalmologists. The results of the conducted experiments showed an efficient and effective in sensitivity, specificity and accuracy.
关键词： micro-aneurysms,DIARETDB1,diabetic retinopathy,exudates,retinal hemorrhage
更新于2025-09-10 09:29:36
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[IEEE 2018 37th Chinese Control Conference (CCC) - Wuhan (2018.7.25-2018.7.27)] 2018 37th Chinese Control Conference (CCC) - Computer Aided Diagnosis for Diabetic Retinopathy based on Fundus Image
摘要： Diabetic Retinopathy (DR) is an eye abnormality caused by long term diabetes, which can lead to vision defects or even blindness. Performing retinal screening examinations on all diabetic patients is a hard work due to the limited number of specialists cannot keep up with the increasing prevalence of diabetes, and hence there are many undiagnosed and untreated cases of DR. Computer aided diagnosis (CAD) is a good way to save the patient’s vision and to help the ophthalmologists in mass screening of diabetes sufferers. The main purpose of the proposed study is to design an automated grading approach for DR screening, using a publicly available database of retinal images, which can evaluate the fundus images like human experts while achieving a high sensitivity for the detection of DR. This paper gives a summary of the results obtained by our previous studies. Together with these results and the DR grading schemes, we provide an automatic analysis approach for the risk of macular edema and the retinopathy grade. Experimental results indicate that the performance of our approach on this database is comparable to that of human experts.
关键词： Optic disc,Macula,Diabetic Retinopathy,Pattern Recognition,Exudates
更新于2025-09-10 09:29:36
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[IEEE 2018 International Symposium ELMAR - Zadar, Croatia (2018.9.16-2018.9.19)] 2018 International Symposium ELMAR - Bright Lesions Detection on Retinal Images by Convolutional Neural Network
摘要： This paper is focused on automatic detection and classification of diabetic retinopathy symptoms, more specifically on the bright lesions (soft and hard exudates) as one of the primary signs suitable for diabetic retinopathy screening. We use a convolutional neural network (CNN) for bright lesions detection and evaluate achieved results using criterion based on proper comparison of each lesion with ground truth images scored by the ophthalmologist. As input data we use original and geometrically transformed retinal images from Messidor database divided into smaller blocks. In that way we enlarge the training dataset and increase classification accuracy.
关键词： Soft and hard exudates classification,Evaluation method,Retinal image,CNN,Messidor database
更新于2025-09-09 09:28:46
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[IEEE 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) - Bangalore, India (2018.9.19-2018.9.22)] 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) - Detection of Exudates in Diabetic Retinopathy
摘要： Diabetic Retinopathy (DR) is an eye abnormality in which the human retina will get affected and is becoming one of the leading cause of preventable blindness. In the world, it is found that nearly 4.8% of blindness is caused due to DR. Preliminary symptoms include the formation of microaneurysms, exudates and hemorrhages. Early detection of DR can save the vision of diabetes patients and manual diagnosis takes time and effort for confirmation. In this paper, a Computer-aided Automated Diagnosis (CAD) is developed to solve this problem. The proposed approach uses edge-based segmentation method for segmenting the optic disc and blood vessels more accurately than region-based methods, followed by extraction of most probable exudates regions, feature extraction and the classifier stage to detect the presence of exudates. This system achieved sensitivity 82.61%, specificity 92.31% and moreover an accuracy of 87.75% for DIARETDB dataset.
关键词： Blindness,edge-based segmentation,Diabetic Retinopathy,CAD,exudates,NPDR,PDR
更新于2025-09-09 09:28:46
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[IEEE 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Coimbatore, India (2018.3.1-2018.3.3)] 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Detecting Hard Exudates In Retinal Fundus Images Using Convolutinal Neural Networks
摘要： The main objective of this project is to detect Exudates in retinal fundus images using Convolutional Neural Networks. Disorders in Retinal Images like Micro aneurysm, Hemorrhages, Hard Exudates, Soft Exudates, Macular Edema, Red lesions, Diabetic Retinopathy are likely to lead to severe visual loss Impairments. This work provides an automatic image processing techniques to diagnose Exudates in human eye and discussed various approaches used to detect Exudates in retinal images. Various publically available databases are listed and provide comparison between different approaches like SVM, KNN and CNN. Diabetic Retinopathy (DR) is the most essential causes of imaginative and prescient loss in diabetic patients. The most primary sign of DR is the presence of exudates, and detecting these in early screening is crucial in preventing vision loss. The automatic reputation of DR consisting of lesions, they are hard exudates (HEs), in fundus pix can make contributions to the diagnosis of this disease. On this take a look at, a fixed of functions from image regions are extracted and decided on the subset which quality discriminates between HEs and the retinal historical past. In proposed system, threshold based segmentation is used for extracting the features. After that, HOG (histogram of gradient), Classify the diseases using the convolutional Neural Network (CNN) classifier. The publicly available STARE of color fundus images was used for testing purposes and the values of sensitivity, specificity and accuracy were found as 96%, 98% and 99.68% respectively for the neural network based classification.
关键词： Hemorrhages,Diabetic Retinopathy,Edema,Microaneurysms,Red lesions,Retinopathy,Exudates
更新于2025-09-04 15:30:14
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Hard exudate based severity assessment of diabetic macular edema from retinal fundus images
摘要： Diabetic macular edema (DME) is a consequence of diabetic retinopathy characterised by the abnormal accumulation of fluid and protein deposit in the macula region of the retina. Prior disclosure of even a trivial trace of DME is essential as it could consequently lead to blurred vision. DME can be diagnosed by the presence of exudates (glossy lesions) in the retinal fundus images. In this work, OD and macula are detected using morphological operation and hard exudates are segmented. Exudates are classified using early treatment diabetic retinopathy standard as normal, moderate or severe cases. The proposed work also incorporates the extraction of various features from the retinal fundus image. Various textural and exudate features are extracted and fed to a classifier to detect DME. Experiments are performed on a publically available database. Performance is evaluated with metrics like accuracy, sensitivity, specificity and accuracy. The results obtained are promising.
关键词： DME,random forest,hard exudates,diabetic macular edema,classification,macula,feature extraction,optic disc
更新于2025-09-04 15:30:14