Evaluation of 3-D Shape Reconstruction of Retinal Fundus

Choe, Tae Eun; Cohen, Isaac; Medioni, Gérard; Walsh, Alexander C.; Sadda, Srinivas R.

doi:10.1007/11866565_17

Cited by 8 publications

(18 citation statements)

References 15 publications

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“…Then, a Parzen window-based mutual information was used to generate dense disparity map. Promising 3D retinal reconstruction results were reported in [3]. However, the stereo reconstruction technique does not work for ETDRS stereo image pairs due to two problems.…”

Section: Introductionmentioning

confidence: 94%

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Affine Camera for 3-D Retinal Surface Reconstruction

Chanwimaluang

Fan

2006

Advances in Visual Computing

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Abstract. We study 3D retinal surface reconstruction by using an affine camera due to two following reasons: (1) NIH's retinal imaging protocols specify a narrow field of view and (2) each retinal image has small depth variation. Specifically, we incorporate the prior knowledge of human retina geometry in the reconstruction process, and introduce a pointbased approach to estimate the retinal spherical surface. We also show that lens distortion removal and affine bundle adjustment improve the reconstruction error in terms of the deviation from the underling spherical surface. Simulation results on both synthetic data and real images show the effectiveness and robustness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 94%

“…al. [3] used PCA-based directional filters to extract candidate seed points (Y features), and plane-and-parallax was employed to estimate the epipolar geometry based on which the stereo pair was rectified. Then, a Parzen window-based mutual information was used to generate dense disparity map.…”

Section: Introductionmentioning

confidence: 99%

Affine Camera for 3-D Retinal Surface Reconstruction

Chanwimaluang

Fan

2006

Advances in Visual Computing

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“…As the model has 7 unknown parameters, at least 7 matching pairs of points are required to estimate these parameters. In [7], the author proposes a robust algorithm to detect special features in the retinal fundus image. This algorithm is based on the detection of intersection points between blood vessels called Y-feature points.…”

Section: Identification Of Disparity Geometric Model Parametersmentioning

confidence: 99%

“…In [3], the authors estimate the epipolar geometry and projection matrices after a self-calibration, then they solve the correspondence problem to reconstruct the 3D fundus surface. In [7], the authors apply a plane+parallax algorithm to stereo images, which is followed by a mutual information-based disparity search stage. In [8], the authors use a multi-focusing technique to capture retinal fundus images and reconstruct its 3D surface.…”

Section: Introductionmentioning

confidence: 99%

Geometrically constrained sub-pixel disparity estimation from stereo images of the retinal fundus

Kharboutly¹,

Vázquez²,

Coulombe³

et al. 2016

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The aim of this study is to propose a precise disparity estimation process between retinal fundus stereoscopic images to assist during eye diagnosis and view generation for visualization. We propose a computer-vision-based solution that allows, from stereo images, the extraction of precise disparity information that could be used for clinical parameters estimation and/or the generation of multi-viewpoint images of retinal fundus. The proposed solution is based on a sub-pixel disparity estimation algorithm that takes into consideration the spherical shape of the retinal fundus. A sub-pixel approach is adopted in order to achieve high precision disparity estimation. Moreover, the a priori knowledge of the fundus geometric shape provides useful information for the regularization of the high precision disparity estimation process. Stereo images, with known ground truth, are used to evaluate the proposed algorithm and to demonstrate the high precision of estimated disparity in our methods.

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“…Although retinal image processing is a well studied field, there are a few studies about 3D reconstruction of retinal fundus [3,4,5,6,7,8]. Liu et al [3] estimate the epipolar geometry and projection matrices after a self calibration.…”

Section: Introductionmentioning

confidence: 99%

Analysis of shape assumptions in 3D reconstruction of retina from multiple fundus images

Ataer-Cansızoğlu

Taguchi

Kalpathy–Cramer

et al. 2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

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Utilizing priors about the shape of retinal surface is important for accurate reconstruction. We present a detailed analysis of geometrical shape priors in the 3D reconstruction of retina. We first approximate the retinal surface either as a sphere inspired by the actual shape of the eyeball, or as a plane inspired by the 2D mosaicing approaches. Based on this approximation, we perform an initial camera localization with a 2D-to-3D registration procedure. Then, parameters of the surface and the camera poses are refined through a nonlinear least squares optimization using different shape priors. The resulting 3D model and camera poses can be used for intuitively visualizing the retinal images with a model-guided browsing interface.

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Evaluation of 3-D Shape Reconstruction of Retinal Fundus

Cited by 8 publications

References 15 publications

Affine Camera for 3-D Retinal Surface Reconstruction

Affine Camera for 3-D Retinal Surface Reconstruction

Geometrically constrained sub-pixel disparity estimation from stereo images of the retinal fundus

Analysis of shape assumptions in 3D reconstruction of retina from multiple fundus images

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