Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing

Estrada, Rolando; Tomasi, Carlo; Cabrera, Michelle T.; Wallace, David K.; Freedman, Sharon F.; Farsiu, Sina

doi:10.1364/boe.2.002871

Cited by 26 publications

(34 citation statements)

References 47 publications

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“…A postprocessing, including Gabor filter and multiscale vessel enhancement filter, was performed to improve the image quality of the en face OCTA. 29, 30 …”

Section: Within-frame Registrationmentioning

confidence: 99%

Automated three-dimensional registration and volume rebuilding for wide-field angiographic and structural optical coherence tomography

et al. 2017

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, "Automated three-dimensional registration and volume rebuilding for wide-field angiographic and structural optical coherence tomography," J. Biomed. Opt. Abstract. We propose a three-dimensional (3-D) registration method to correct motion artifacts and construct the volume structure for angiographic and structural optical coherence tomography (OCT). This algorithm is particularly suitable for the nonorthogonal wide-field OCT scan acquired by a ultrahigh-speed swept-source system (>200 kHz A-scan rate). First, the transverse motion artifacts are corrected by the between-frame registration based on en face OCT angiography (OCTA). After A-scan transverse translation between B-frames, the axial motions are corrected based on the rebuilt boundary of inner limiting membrane. Finally, a withinframe registration is performed for local optimization based on cross-sectional OCTA. We evaluated this algorithm on retinal volumes of six normal subjects. The results showed significantly improved retinal smoothness in 3-D-registered structural OCT and image contrast on en face OCTA.

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“…A postprocessing, including Gabor filter and multiscale vessel enhancement filter, was performed to improve the image quality of the en face OCTA. 29, 30 …”

Section: Within-frame Registrationmentioning

confidence: 99%

Automated three-dimensional registration and volume rebuilding for wide-field angiographic and structural optical coherence tomography

et al. 2017

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“…Gabor filter was applied to enhance the connectivity and facticity of the vasculature (Fig. 4(D)) [25]. The en face image was divided into microsaccade-free strips at the detected motion lines (Fig.…”

Section: Motion Detection and Strip Divisionmentioning

confidence: 99%

Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram

Zang¹,

Liu

Zhang

et al. 2016

Biomed. Opt. Express

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Abstract:We propose an innovative registration method to correct motion artifacts for widefield optical coherence tomography angiography (OCTA) acquired by ultrahigh-speed sweptsource OCT (>200 kHz A-scan rate). Considering that the number of A-scans along the fast axis is much higher than the number of positions along slow axis in the wide-field OCTA scan, a non-orthogonal scheme is introduced. Two en face angiograms in the vertical priority (2 y-fast) are divided into microsaccade-free parallel strips. A gross registration based on large vessels and a fine registration based on small vessels are sequentially applied to register parallel strips into a composite image. This technique is extended to automatically montage individual registered, motion-free angiograms into an ultrawide-field view. Werner, "En face projection imaging of the human choroidal layers with tracking SLO and swept source OCT angiography methods," Proc. SPIE 9541, 954112 (2015).

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“…Next, to enhance the contrast between vessel and non-vessel pixels in both SVPs, we employ a multi-scale approach using Laplacian-of-Gaussian (LoG) filters and Gabor wavelets, as detailed in [33,34]. Briefly, this method first convolves the SVP with a bank of LoG filters of various standard deviations, and keeps the maximum response at each pixel.…”

Section: Vessel Segmentationmentioning

confidence: 99%

Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

Srinivasan

Heflin

Izatt

et al. 2014

Biomed. Opt. Express

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124

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Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software.

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Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing

Cited by 26 publications

References 47 publications

Automated three-dimensional registration and volume rebuilding for wide-field angiographic and structural optical coherence tomography

Automated three-dimensional registration and volume rebuilding for wide-field angiographic and structural optical coherence tomography

Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram

Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

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