Journal ArticleDOI
An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images
TLDR
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).Abstract:
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 in diabetic retinopathy 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.read more
Citations
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Journal ArticleDOI
Fuzzy based image edge detection algorithm for blood vessel detection in retinal images
TL;DR: A contour detection based image processing algorithm based on Mamdani (Type-2) fuzzy rules for detection of blood vessels in retinal fundus images that offers an improved dynamics and flexibility in formulation of the linguistic threshold criteria.
Journal ArticleDOI
Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier
Lakshmana Kumar Ramasamy,Shynu Gopalan Padinjappurathu,Seifedine Kadry,Robertas Damaševičius +3 more
TL;DR: Wang et al. as mentioned in this paper extracted and fused the ophthalmoscopic features from the retina images based on textural gray-level features like co-occurrence, run-length matrix, as well as the coefficients of the Ridgelet Transform.
Journal ArticleDOI
Deep Learning Techniques For Diabetic Retinopathy Classification: A Survey
TL;DR: This paper reviews and analyzes state-of-the-art deep learning methods in supervised, self-supervised, Vision Transformer etc. setups, proposing retinal fundus image classification and detection of Diabetic Retinopathy and assesses research gaps.
Journal ArticleDOI
Deep Learning Techniques for Diabetic Retinopathy Classification: A Survey
TL;DR: In this paper , state-of-the-art deep learning methods in supervised, self-supervised, and Vision Transformer setups, proposing retinal fundus image classification and detection.
Journal ArticleDOI
A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection
TL;DR: A comprehensive computer-aided diagnostic (CAD) system that exploits the MLC of DR grades using colored fundus photography to detect and analyzes various retina pathological changes accompanying DR development shows promising results.
References
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Automated feature extraction in color retinal images by a model based approach
Huiqi Li,Opas Chutatape +1 more
TL;DR: Novel methods to extract the main features in color retinal images have been developed and an approach to detect exudates by the combined region growing and edge detection is proposed.
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Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering
TL;DR: An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed and finds that the proposed method detectsExudates successfully with sensitivity, specificity, PPV, PLR and accuracy.
Journal ArticleDOI
Retinal Microaneurysm Detection Through Local Rotating Cross-Section Profile Analysis
István Lázár,Andras Hajdu +1 more
TL;DR: A method for the automatic detection of microaneurysms (MAs) in color retinal images through the analysis of directional cross-section profiles centered on the local maximum pixels of the preprocessed image is proposed.
Journal ArticleDOI
Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images
TL;DR: A novel splat feature classification method is presented with application to retinal hemorrhage detection in fundus images, and has potential to be applied to other object detection tasks.
Journal ArticleDOI
Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy
Harihar Narasimha-Iyer,Ali Can,Badrinath Roysam,V. Stewart,H.L. Tanenbaum,A. Majerovics,Hanumant Singh +6 more
TL;DR: A fully automated approach to robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy, focusing on diabetic changes, has broader applicability in ophthalmology.