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Efficacy of navigated focal laser photocoagulation in diabetic macular edema planned with en face optical coherence tomography versus fluorescein angiography
摘要： Aim To analyze the efficacy of navigated focal laser photocoagulation (FLP) of microaneurysms in diabetic macular edema (DME) planned using en face optical coherence tomography (OCT) as against fluorescein angiography (FA). Methods Twenty-six eyes of 21 DME patients (12 males, 9 females, 69.5 ± 12.3 years) with mean BCVA of 0.52 ± 0.44 LogMAR were included. En face OCT images of deep capillary plexus slab and FA images were used to plan FLP targeting of leaky microaneurysms. The primary outcome measures were central retinal thickness (CRT) and macular volume. The secondary outcome measure was best-corrected visual acuity (BCVA). Results The difference in the change of CRT and macular volume between en face OCT and FA-planned FLP after 1 month and at the end of follow-up was not statistically significant (p > 0.05), except for a higher CRT reduction in the en face OCT-planning group (p = 0.007) at the end of mean follow-up of 2.6 ± 0.9 months. There was no difference in BCVA change between the two planning options (p = 0.42). Conclusion En face OCT is a non-inferior alternative for FA in the planning of navigated FLP of microaneurysms in DME.
关键词： Navigated laser,Optical coherence tomography,Diabetic macular edema,Focal laser photocoagulation,Optical coherence tomography angiography,Fluorescein angiography,Microaneurysms
更新于2025-09-23 15:19:57
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Severity analysis of diabetic retinopathy in retinal images using hybrid structure descriptor and modified CNNs
摘要： Imaging which plays a central role in the diagnosis and treatment planning of diabetic retinopathy and severity is an important diagnostic indicator in treatment planning and results assessment. Retinal image classification is an increasing attention among researchers in the field of computer vision, as it plays an important role in disease diagnosis. Computer Aided Diagnosis (CAD) is in wide practice in clinical work for the location and anticipation of different kinds of variations; the automated image classification systems used for such applications must be significantly efficient in terms of accuracy since false detection may lead to fatal results. Another requirement is the high convergence rate which accounts for the practical feasibility of the system. The overall classification accuracy of the proposed HTF with MCNNs is 98.41%, but the existing methods HTF with SVM and HTF with CNNs produce 97.84% and 96.65% respectively.
关键词： Segmentation,SVM,Medical image processing,Microaneurysms,Diabetic retinopathy,Classification
更新于2025-09-23 15:19:57
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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