Stephen R. Fransen

Hybrid retinal image registration

This work studies retinal image registration in the context of the National Institutes of Health (NIH) Early Treatment Diabetic Retinopathy Study (ETDRS) standard. The ETDRS imaging protocol specifies seven fields of each retina and presents three major challenges for the image registration task. First, small overlaps between adjacent fields lead to inadequate landmark points for feature-based methods. Second, the non-uniform contrast/intensity distributions due to imperfect data acquisition will deteriorate the performance of area-based techniques. Third, high-resolution images contain large homogeneous nonvascular/texureless regions that weaken the capabilities of both feature-based and area-based techniques. In this work, we propose a hybrid retinal image registration approach for ETDRS images that effectively combines both area-based and feature-based methods. Four major steps are involved. First, the vascular tree is extracted by using an efficient local entropy-based thresholding technique. Next, zeroth-order translation is estimated by maximizing mutual information based on the binary image pair (area-based). Then image quality assessment regarding the ETDRS field definition is performed based on the translation model. If the image pair is accepted, higher-order transformations will be involved. Specifically, we use two types of features, landmark points and sampling points, for affine/quadratic model estimation. Three empirical conditions are derived experimentally to control the algorithm progress, so that we can achieve the lowest registration error and the highest success rate. Simulation results on 504 pairs of ETDRS images show the effectiveness and robustness of the proposed algorithm

A novel device for accurate and efficient testing for vision-threatening diabetic retinopathy

AIMS To evaluate the performance of the RETeval device, a handheld instrument using flicker electroretinography (ERG) and pupillography on undilated subjects with diabetes, to detect vision-threatening diabetic retinopathy (VTDR). METHODS Performance was measured using a cross-sectional, single armed, non-interventional, multi-site study with Early Treatment Diabetic Retinopathy Study 7-standard field, stereo, color fundus photography as the gold standard. The 468 subjects were randomized to a calibration phase (80%), whose ERG and pupillary waveforms were used to formulate an equation correlating with the presence of VTDR, and a validation phase (20%), used to independently validate that equation. The primary outcome was the prevalence-corrected area under the receiver operating characteristic (ROC) curve for the detection of VTDR. RESULTS The area under the ROC curve was 0.86 for VTDR. With a sensitivity of 83%, the specificity was 78% and the negative predictive value was 99%. The average testing time was 2.3 min. CONCLUSIONS With a VTDR prevalence similar to that in the U.S., the RETeval device will identify about 75% of the population as not having VTDR with 99% accuracy. The device is simple to use, does not require pupil dilation, and has a short testing time.

Correction to "Hybrid Retinal Image Registration"

The purpose of this note is to make a correction to the blood vessel segmentation algorithm discussed in the above titled paper (ibid., vol 1, no. 1, pp. 129-142, Jan 06).

3-D Retinal Curvature Estimation

We study 3-D retinal curvature estimation from multiple images that provides the fundamental geometry of the human retina and could be used for 3-D retina visualization and disease diagnosis purposes. An affine camera model is used for 3-D reconstruction due to its simplicity, linearity, and robustness. A major challenge is that a series of optics is involved in the retinal imaging process, including an actual fundus camera, a digital camera, and the optics of the human eye, all of which cause significant nonlinear distortions in retinal images. In this paper, we develop a new constrained optimization method that considers both the geometric shape of the human retina and nonlinear lens distortions. Moreover, we examine a variety of lens distortion models to approximate the optics of the human eye in order to create a smooth spherical surface for curvature estimation. The experimental results on both synthetic data and real retinal images validate the proposed algorithm.

The Effect of Phenylephrine on Müller Muscle

PURPOSE The blepharogram technique is used to study the effect of a drug on blinking. The authors show that ocular instillation of phenylephrine, a stimulant of Müller muscle of the eyelid, accelerates the up phase of the blink. METHODS Motion of a tiny search coil glued to the eyelid moving in a weak magnetic field modifies an induced alternating current which is amplified and used to display the position of the upper eyelid in degrees on the ordinate of a graph with time in milliseconds on the abscissa. The graph is called a blepharogram. Blepharogram studies and individual blink analysis show the effect of phenylephrine on eyelid motion (blinking). RESULTS Instillation of phenylephrine accelerated the up phase of the blink in all ten experimental subjects. In 65% of subjects, phenylephrine also produced or increased newly described N and M blepharogram patterns. CONCLUSION This is the first instrumental detection of the effect of a pharmacologic agent on eyelid motion. The blepharogram technique provides insight into eyelid physiology and can be used to study any neuromuscular condition that affects eyelid motion.