Feasibility of level-set analysis of enface OCT retinal images in diabetic retinopathy

Mohammad, Fatimah; Ansari, Rashid; Wanek, Justin; Francis, Andrew; Shahidi, Mahnaz

doi:10.1364/boe.6.001904

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…Level-set methods are widely used in image segmentation [27][28][29], and has recently been applied to detect abnormality in OCT en face images [30]. Level-set method represents the boundary of interest in image I as contour φ = 0 (i.e.…”

Section: Fuzzy Level-set Methodsmentioning

confidence: 99%

Automated volumetric segmentation of retinal fluid on optical coherence tomography

Wang

Zhang

Pechauer

et al. 2016

Biomed. Opt. Express

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Abstract:We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation.

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Section: Fuzzy Level-set Methodsmentioning

confidence: 99%

Automated volumetric segmentation of retinal fluid on optical coherence tomography

Wang

Zhang

Pechauer

et al. 2016

Biomed. Opt. Express

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“… 19 – 30 We previously reported methods for generation of en face reflectance images of individual retinal layers from a high-density raster of images. 31 – 33 In the current study, we report for the first time an en face OCT imaging method for quantitative measurements of both thickness and reflectance alterations in individual retinal layers and macular subfields at different stages of DR.…”

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confidence: 95%

Alterations in Retinal Layer Thickness and Reflectance at Different Stages of Diabetic Retinopathy by En Face Optical Coherence Tomography

Wanek

Blair

Chau

et al. 2016

Invest. Ophthalmol. Vis. Sci.

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PurposeThis article reports a method for en face optical coherence tomography (OCT) imaging and quantitative assessment of alterations in both thickness and reflectance of individual retinal layers at different stages of diabetic retinopathy (DR).MethodsHigh-density OCT raster volume scans were acquired in 29 diabetic subjects divided into no DR (NDR) or non-proliferative DR (NPDR) groups and 22 control subjects (CNTL). A customized image segmentation method identified eight retinal layer interfaces and generated en face thickness maps and reflectance images for nerve fiber layer (NFL), ganglion cell and inner plexiform layers (GCLIPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptor outer segment layer (OSL), and retinal pigment epithelium (RPE). Mean thickness and intensity values were calculated in nine macular subfields for each retinal layer.ResultsEn face thickness maps and reflectance images of retinal layers in CNTL subjects corresponded to normal retinal anatomy. Total retinal thickness correlated negatively with age in nasal subfields (R ≤−0.31; P ≤ 0.03, N = 51). In NDR subjects, NFL and OPL thickness were decreased (P = 0.05), and ONL thickness was increased (P = 0.04) compared to CNTL. In NPDR subjects, GCLIPL thickness was increased in perifoveal subfields (P < 0.05) and INL intensity was higher in all macular subfields (P = 0.04) compared to CNTL.ConclusionsDepth and spatially resolved retinal thickness and reflectance measurements are potential biomarkers for assessment and monitoring of DR.

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“…Over recent decades, the utilizations of computer vision technique on OCT images concentrate on medical filed 13 14 . Most applications focus on monitoring the health status of vivo tissue, such as eye, arteria coronaria, esophagus, gastrointestinal tract.…”

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confidence: 99%

Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images

Zhou

Liu²,

Shi

et al. 2016

Sci Rep

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Optical coherence tomography (OCT) has been applied to inspect the internal defect of beadless Chinese ZhuJi fleshwater pearls. A novel fully automated algorithm is proposed to classify between normal and defective sub-layer in nacre layer. Our algorithm utilizes the graph segmentation approach to estimate the up and down boundaries of defect sub-layers from flattened and cropped image, and also proposes the strategy for edge and weight construction in segmentation process. The vertical gradients of boundary pixels are used to make grading decision. The algorithm is tested by typical pearl samples, and achieves 100% classification accuracy. The experiment result shows the feasibility and adaptability of the proposed approach, and proves that the OCT technique combined with proposed algorithm is a potential tool for fast and non-destructive diagnosis of internal structure of beadless pearl.

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Feasibility of level-set analysis of enface OCT retinal images in diabetic retinopathy

Cited by 12 publications

References 28 publications

Automated volumetric segmentation of retinal fluid on optical coherence tomography

Automated volumetric segmentation of retinal fluid on optical coherence tomography

Alterations in Retinal Layer Thickness and Reflectance at Different Stages of Diabetic Retinopathy by En Face Optical Coherence Tomography

Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images

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