The fovea is one of the most significant regions of the human eye. Accurate localization of the fovea is very helpful to downstream computer-aided diagnosis analysis in retinal imaging. Most previous researches have focused on fovea localization for the color fundus imaging. In this paper, we propose a three-step process to localize the foveal center and the foveal region for three retinal modalities: color fundus, Infrared (IR), and optical coherence tomography (OCT). From the experimental results, the proposed method achieves accurate fovea localization for multimodal imaging.
A multistage algorithm is presented, whose components are based upon maximum likelihood estimation (MLE). From
3D scanning laser ophthalmoscope (SLO) image data, the algorithm finds the positions of the two anatomical boundaries
of the eye's fundus that define the retina, which are the internal limiting membrane (ILM) and the retinal pigment
epithelium (RPE). he retinal thickness is then calculated by subtraction. Retinal thickness is useful for indicating,
assessing risk of, and following several diseases, including various forms of macular edema and cysts.
Purpose: To study the frequency and severity of artifacts in optical Coherence tomography images and to develop a new algorithm for improved retinal thickness detection.
Methods: We propose a new method to measure the retinal thickness in OCT scans. We compared our modified edge detection (MED) method to the Markov method and the conventional OCT algorithm (cOCT) in 226 OCT macular scans.
Results: We defined errors as a difference in detected interface location of less than 100 µm offset for less than 10 A-scans, otherwise it was an artifact. The frequency of errors was reduced from 32% (cOCT) to less than 2% with the MED method, while the Markov method had a frequency of 5%. Artifacts were reduced from 9.3% (cOCT) to 0.9% (MED) while the Markov method had a frequency of 11.5%.
Conclusion: The results show the MED method of detecting retinal thickness is superior to the other two methods, since the OCT method is prone to both errors and artifacts and the Markov method is robust only to healthy retina. Our MED method is robust for detection of normal retinas and effective even in eyes with pathological conditions. Use of improved retinal thickness detection algorithm should significantly improve clinical utility of the optical coherence tomograph.
The purpose of this study is to determine the effects of the working distance on the accuracy of confocal scanning laser tomography using the Heidelberg Retina Tomograph II. Twenty eyes of normal patients were imaged and the topographies of the retinal surfaces were recorded. Each eye was imaged first at the optimum working distance,
establishing the baseline exam, and then re-imaged at four different working distances (one at a shorter distance than optimum, three more at longer distances than optimum, variation done in 2 mm increments). The recorded data at various working distances was compared to the baseline data. The deviation from the baseline was compared to the
normal standard deviation for the instrument reported in the literature. Data is within the normal standard deviation when staying between -2 mm and +4 mm of optimum working distance. Some stereometric parameters vary greater than the normal standard deviation if working distance is more than +4 mm from optimum. To minimize error in recorded data, the operator of the Heidelberg Retinal Tomograph II should image the patient’s eye between -2 mm and
+4 mm of optimum working distance. Staying in this range should provide results that vary within the normal standard deviation.
The purpose of this study was the experimental determination of the type of wave aberration corrected by the micro-machined continuous-membrane deformable mirror from OKO Technologies, and the determination of the limitation in the dynamic range of its correction. We wanted to compare these characteristics with the requirements met in vision science and we wanted to be able to judge the capacity and performance of this deformable mirror for this field of application. To characterize the quality of the static aberration correction of the system, we used phase plates simulating astigmatism and higher order aberrations for an artificial eye consisting of a lens and an USAF resolution target. The pupil size used for the scanning area was 6 mm. The adaptive optics system worked as a closed-loop. Our methodology consisted of measuring the aberrations of these plates and subsequently comparing them with the exact specification given by the manufacturer. In a second time, we corrected the wave front and analyze the quality of the correction by comparing the total root mean square wave front error and the Strehl ratio before and after compensation. We also studied the speed of convergence of the system. We then compared the experimental results with the theoretical simulations of the mirror behavior from other publications.
We have investigated the use of a 19-channel micromachined membrane deformable mirror (MMDM) for correcting aberrations of the eye to improve the resolution of fundus imaging. A Hartmann-Shack wavefront sensor (HSWS) and the MMDM are used to measure and correct aberrations existing in the anterior segments of the eye, respectively. Zernike polynomials are used to represent the MMDM surface shape as well as the optical wavefront shape. In order to control the MMDM, which has nonlinear and coupled responses to electrostatic controls, we have developed an adaptive control algorithm to iteratively adjust the control voltages of all channels, thus modulating the shape of the MMDM to reduce the variance of the optical wavefront measured using the HSWS. Experimental results using an artificial eye show that the adaptive system can compensate for low-order and some high- order aberrations, thereby improving the resolution of retinal images. The capability for correcting ocular aberrations is limited by the number of channels and the deflection range of the MMDM. Our new adaptive control algorithm allows effective use of the low-cost, compact MMDM, making adaptive optics a viable and practical technique for clinical high-resolution fundus cameras and other ophthalmic imaging instruments.
A deconvolution algorithm for use with scanning laser ophthalmoscope (SLO) data is being developed. The SLO is fundamentally a confocal microscope in which the objective lens is the human ocular lens. 3D data is collected by raster scanning to form images at different depths in retinal and choroidal layers. In this way, 3D anatomy may be imaged and stored as a series of optical sections.Given the poor optical quality of the human lens and random eye motion during data acquisition, any deconvolution method applied to SLO data must be able to account for distortions present in the observed data. The algorithm presented compensates for image warping and frame-to-frame displacement due to random eye motion, smearing along the optic axis, sensor saturation, and other problems. A preprocessing step is first used to compensate for frame-to-frame image displacement. The image warping, caused by random eye motion during raster scanning, is corrected. Finally, a maximum likelihood based blind deconvolution algorithm is used to correct severe blurring along the optic axis. The blind deconvolution algorithm contains an iterative search for subpixel displacements remaining after image warping and frame-to-frame displacements are corrected. This iterative search is formulated to ensure that the likelihood functional is non-decreasing.
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