1. Articles from Yijun HUANG

    1-12 of 12
    1. Development of a Semi-Automatic Segmentation Method for Retinal OCT Images Tested in Patients with Diabetic Macular Edema

      Development of a Semi-Automatic Segmentation Method for Retinal OCT Images Tested in Patients with Diabetic Macular Edema

      Purpose To develop EdgeSelect, a semi-automatic method for the segmentation of retinal layers in spectral domain optical coherence tomography images, and to compare the segmentation results with a manual method. Methods SD-OCT (Heidelberg Spectralis) scans of 28 eyes (24 patients with diabetic macular edema and 4 normal subjects) were imported into a customized MATLAB application, and were manually segmented by three graders at the layers corresponding to the inner limiting membrane (ILM), the inner segment/ellipsoid interface (ISe), the retinal/retinal pigment epithelium interface (RPE), and the Bruch's membrane (BM). The scans were then segmented independently by the same ...

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    2. Effect of optical coherence tomography scan decentration on macular center subfield thickness measurements

      Effect of optical coherence tomography scan decentration on macular center subfield thickness measurements

      PURPOSE: To investigate the effect of optical coherence tomography macular grid displacement on retinal thickness measurements. METHODS: SD-OCT macular scans of 66 eyes with various retinal thicknesses were selected. Decentration of the 1,3,6 mm-diameter macular grid was simulated by manually adjusting the distance between center of the fovea (cFovea) and center of the grid (cGrid). Center subfield thickness (CSF) between the internal limiting membrane and the top of the retinal pigment epithelium was measured along the displacement distance where the grid was displaced in 8 cardinal directions from the cFovea in steps of 100 μm within the central ...

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    3. Mapping of retinal parameters from combined fundus image and three-dimensional optical coherence tomography

      Mapping of retinal parameters from combined fundus image and three-dimensional optical coherence tomography

      A second retinal characterization data set is mapped to a first retinal characterization dataset. The first retinal characterization dataset is displayed as a first graphical map. The second retinal characterization dataset is displayed as a second graphical map which is mapped to the first graphical map. The second graphical map may be warped and morphed onto the first graphical map. Retinal characterization datasets may be derived either from a fundus image or from a retinal parameter dataset calculated from a three-dimensional optical coherence tomography scan of a retina. Retinal parameter datasets may characterize parameters such as retinal thickness. In an ...

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    4. Circular profile mapping and display of retinal parameters

      Circular profile mapping and display of retinal parameters

      Certain diseases of the retina are diagnosed by circular profile analysis of retinal parameters, such as thickness. Retinal thickness around a user-defined circle on the retina is measured by various ophthalmological techniques and--+mapped to a circular profile map. The circular profile map does not use segmentation of measurement data into arbitrary arcs, and thickness is mapped to a quasi-continuous range of display bands. The circular profile map is superimposed on a fundus image, or other two-dimensional image of the retina, allowing association of the circular profile map with the presence of blood vessels and other anatomical features. The simultaneous ...

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    5. Signal quality assessment of retinal optical coherence tomography (OCT) images

      Signal quality assessment of retinal optical coherence tomography (OCT) images

      Purpose: To assess signal quality of retinal OCT images from multiple devices using subjective and quantitative measurements. Methods: 120 multi-frame OCT images from 4 SD OCT devices (Cirrus, RTVue, Spectralis, and 3D OCT-1000) were evaluated subjectively by trained graders, and measured quantitatively using a derived parameter, maximum tissue contrast index (mTCI). An intensity histogram decomposition model was proposed to separate the foreground and background information of OCT images and to calculate the mTCI. The mTCI results were compared to the manufacturer signal index (MSI) provided by the respective devices, and to the subjective grading scores (SGS). Results: Statistically significant correlations ...

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    6. Method and apparatus for spatially mapping three-dimensional optical coherence tomography data with two-dimensional images

      Method and apparatus for spatially mapping three-dimensional optical coherence tomography data with two-dimensional images

      Voxel data from a three-dimensional optical coherence tomography (3-D OCT) scan of a retina and pixel data from a two-dimensional (2-D) fundus image are spatially mapped. A 2-D composite image generated from the 3-D OCT data is spatially mapped to a fundus image using spatial indicia common to both images. The 3-D OCT data is then spatially mapped to the fundus image. An image processing system generates cross-correlated graphical representations of 3-D OCT data, subsets of 3-D OCT data, and a fundus image.

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    7. Retinal thickness measurement by combined fundus image and three-dimensional optical coherence tomography

      Retinal thickness measurement by combined fundus image and three-dimensional optical coherence tomography
      Disclosed are method and apparatus for mapping retinal thickness values to a movable measurement grid. A three-dimensional volume dataset acquired from three-dimensional optical coherence tomography is registered to a fundus image by rendering a two-dimensional composite image from the three-dimensional volume dataset and superimposing characteristic features in the two-dimensional composite image upon corresponding characteristic features in the fundus image. A measurement grid is displayed on the two-dimensional composite image. The measurement grid is moved to a region of interest, and retinal thickness values in the region of interest are mapped to sectors within the measurement grid.
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    8. Characterization of retinal parameters by circular profile analysis

      Characterization of retinal parameters by circular profile analysis
      Certain diseases of the retina are diagnosed by circular profile analysis of retinal parameters, such as thickness. Retinal thickness around a user-defined circle on the retina is measured by three-dimensional optical coherence tomography or other ophthalmological techniques. Abnormally thin regions are identified by comparing a measured function of thickness vs. polar angle to a reference function of thickness vs. polar angle. A degree of abnormality is characterized by the ratio of the integral of the measured thickness function to the integral of the reference thickness function over the abnormally thin region, as specified by a range of polar angles.
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    9. Characterization of the retinal nerve fiber layer

      Characterization of the retinal nerve fiber layer
      Disclosed are method and apparatus for characterizing the retinal nerve fiber layer (RNFL). An advantageous diagnostic parameter for characterizing the RNFL is a function of the product of the local RNFL thickness at a measurement locus.times.the distance of the measurement locus from a base point. The value of the diagnostic parameter in a patient's retina is compared to a corresponding reference range acquired from a population of healthy retinas.
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    10. Ophthalmologic information processing apparatus and ophthalmologic examination apparatus

      An ophthalmologic examination apparatus 1 projects a light onto a fundus oculi, detects the reflected light thereof, and forms a 3-dimensional image that represents the morphology of a retina based on the detected results. A stimulation-position specifying part 233 specifies, in the 3-dimensional image, a plurality of stimulation positions that correspond to a plurality of stimulation points Pi in a visual-field examination. A layer-thickness measuring part 235 analyzes the 3-dimensional image to find the layer thickness of the retina at each stimulation position. In addition, a displacement calculation part 234 specifies a related position of the stimulation position. A layer-thickness ...
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    11. Retinal Thickness Measurement By Combined Fundus Image And Three-Dimensional Optical Coherence Tomography (Wo 2009/061424)

      Retinal Thickness Measurement By Combined Fundus Image And Three-Dimensional Optical Coherence Tomography (Wo 2009/061424)
      Disclosed are method and apparatus for mapping retinal thickness values to a movable measurement grid. A three-dimensional volume dataset acquired from three- dimensional optical coherence tomography is registered to a fundus image by rendering a two-dimensional composite image from the three-dimensional volume dataset and superimposing characteristic features in the two-dimensional composite image upon corresponding characteristic features in the fundus image. A measurement grid is displayed on the two-dimensional composite image. The measurement grid is moved to a region of interest, and retinal thickness values in the region of interest are mapped to sectors within the measurement grid.
      Read Full Article
    12. Mapping Of Retinal Parameters From Combined Fundus Image And Three-Dimensional Optical Coherence Tomography (Wo 2009/061425)

      A second retinal characterization data set is mapped to a first retinal characterization dataset. The first retinal characterization dataset is displayed as a first graphical map. The second retinal characterization dataset is displayed as a second graphical map which is mapped to the first graphical map. The second graphical map may be warped and morphed onto the first graphical map. Retinal characterization datasets may be derived either from a fundus image or from a retinal parameter dataset calculated from a three-dimensional optical coherence tomography scan of a retina. Retinal parameter datasets may characterize parameters such as retinal thickness. In an ...
      Read Full Article
    1-12 of 12
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    1. (10 articles) Yijun Huang
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    Retinal Thickness Measurement By Combined Fundus Image And Three-Dimensional Optical Coherence Tomography (Wo 2009/061424) Characterization of the retinal nerve fiber layer Characterization of retinal parameters by circular profile analysis Retinal thickness measurement by combined fundus image and three-dimensional optical coherence tomography Method and apparatus for spatially mapping three-dimensional optical coherence tomography data with two-dimensional images Signal quality assessment of retinal optical coherence tomography (OCT) images Circular profile mapping and display of retinal parameters Mapping of retinal parameters from combined fundus image and three-dimensional optical coherence tomography Principles and Applications of Fourier Optics Design and optimization of a spectrometer for spectral domain optical coherence tomograph Ultra-compact silicon photonic integrated interferometer for swept-source optical coherence tomography Enhanced Depth Imaging Spectral-Domain Optical Coherence Tomography Findings in Choroidal Neurofibromatosis