Quantitative depth analysis of optic nerve head using stereo retinal fundus image pair.

Depth analysis of the optic nerve head (ONH) in the retinal fundus is important for the early detection of glaucoma. In this study, we investigate an automatic reconstruction method for the quantitative depth measurement of the ONH from a stereo retinal fundus image pair. We propose a technique to obtain the depth value from the stereo retinal fundus image pair, which mainly consists of five steps: 1. cutout of the ONH region from the stereo retinal fundus image pair, 2. registration of the stereo image pair, 3. disparity measurement, 4. noise reduction, and 5. quantitative depth calculation. Depth measurements of 12 normal eyes are performed using the stereo fundus camera and the Heidelberg Retina Tomograph (HRT), which is a confocal laser-scanning microscope. The depth values of the ONH obtained from the stereo retinal fundus image pair were in good accordance with the value obtained using HRT (r=0.80+/-0.15). These results indicate that our proposed method could be a useful and easy-to-handle tool for assessing the cup depth of the ONH in routine diagnosis as well as in glaucoma screening.

Rheology of tear film lipid layer spread in normal and aqueous tear-deficient dry eyes.

PURPOSE: To analyze the relationship between tear volume and tear film lipid layer (TFLL) spread. METHODS: Twenty-nine eyes from 22 subjects, including normal eyes and eyes with aqueous tear-deficient dry eye, were enrolled in this study. In all eyes, the radius of curvature (R: mm) of the central lower tear meniscus was measured with a video-meniscometer, and interference images from the TFLL were recorded with a video-interferometer. Interference images were captured as still images every 0.05 second, and the relationship between the acquisition time for each image after a blink and the averaged heights of the spreading TFLL in the upstroke of the blink were calculated. RESULTS: In all cases, the time-dependent changes in TFLL spread could be described by the expression H(t) - H(0) = rho[1 - exp(-t/lambda)], where H(t) is the averaged height in millimeters at time t, H(0) is the averaged height at t = 0, rho is a constant, t is time in seconds, and lambda is the characteristic time in seconds. A statistically significant correlation was found between those changes and the initial upward velocity of the spreading TFLL [H'(0) = dH(0)/dt] and R (r = 0.573; P = 0.003). CONCLUSIONS: This study demonstrated that the time-dependent changes of TFLL spread are compatible with the Voigt model of viscoelasticity and that the initial velocity of TFLL spread after a blink decreased in proportion to the decrease of tear volume. There is potential interest in using this parameter to diagnose and evaluate the severity of aqueous tear deficiency.

Automated detection algorithm for arteriolar narrowing on fundus images.

We have developed a computer-aided diagnosis system (CAD) to detect abnormalities in fundus images. In Japan, ophthalmologists usually detect hypertensive changes by identifying arteriolar narrowing and focal arteriolar narrowing. The purpose of this study is to develop an automated method for detecting arteriolar narrowing and focal arteriolar narrowing on fundus images. The blood vessel candidates were detected by the density analysis method. In blood vessel tracking, a local detection function was used to determine the centerline of the blood vessel. A direction comparison function using three vectors was designed to optimally estimate the next possible location of a blood vessel. After the connectivity of vessel segments was adjusted based on the recognized intersections, the true tree-like structure of the blood vessels was established. The blood vessels were recognized as arteries or veins by hue of HSV color space and their diameters. The arteriolar narrowing was detected by the ratio of diameters (artery vs. vein; A/V ratio). Focal arteriolar narrowing was detected by measuring the diameter of an artery. By applying this method to 100 fundus images, the detection sensitivity for arteriolar narrowing was found to be 76% when the specificity was 91%. Furthermore, by applying this method to 70 other different fundus images, the detection sensitivity for the focal arteriolar narrowing was 75% with 2.9 false positives per image. The number of some false positives is planned to be reduced during the next stage of development. Such an automated detection of abnormal vessels could help ophthalmologists in diagnosing ocular diseases.