ArticleBiomedical engineering online2016
Retinal status analysis method based on feature extraction and quantitative grading in OCT images.
Article in Biomedical engineering online, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 28 citations in OpenAlex.
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- Deep Learning Prediction of Response to Anti-VEGF among Diabetic Macular Edema Patients: Treatment Response Analyzer System (TRAS).Diagnostics (Basel, Switzerland) · 2022Article
- Diurnal changes of retinal microvascular circulation and RNFL thickness measured by optical coherence tomography angiography in patients with obstructive sleep apnea-hypopnea.Frontiers in endocrinology · 2022Article
- Deep learning architecture "LightOCT" for diagnostic decision support using optical coherence tomography images of biological samples.Biomedical optics express · 2020Article
- Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.Biomedical engineering online · 2017Article
- Artificial Intelligence-Based Disease Activity Monitoring to Personalized Neovascular Age-Related Macular Degeneration Treatment: A Feasibility Study.Ophthalmology scienceArticle
- Forming Optimal Projection Images from Intra-Retinal Layers Using Curvelet-Based Image Fusion Method.Journal of medical signals and sensorsArticle
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOptical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis.
methodsThis study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images.
resultsThis paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status.
conclusionsThis paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.
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