R. A. Karlsson

Automatic registration of retina images based on genetic techniques

The aim of this paper is to develop an automatic method for the registration of multitemporal digital images of the fundus of the human retina. The images are acquired from the same patient at different times by a color fundus camera. The proposed approach is based on the application of global optimization techniques to previously extracted maps of curvilinear structures in the images to be registered (such structures being represented by the vessels in the human retina): in particular, a genetic algorithm is used, in order to estimate the optimum transformation between the input and the base image. The algorithm is tested on two different types of data, gray scale and color images, and for both types, images with small changes and with large changes are used. The comparison between the registered images using the implemented method and a manual one points out that the proposed algorithm provides an accurate registration. The convergence to a solution is not possible only when dealing with images taken from very different view-points.

Automatic Retinal Oximetry

PURPOSE To measure hemoglobin oxygen saturation (SO(2)) in retinal vessels and to test the reproducibility and sensitivity of an automatic spectrophotometric oximeter. METHODS Specialized software automatically identifies the retinal blood vessels on fundus images, which are obtained with four different wavelengths of light. The software calculates optical density ratios (ODRs) for each vessel. The reproducibility was evaluated by analyzing five repeated measurements of the same vessels. A linear relationship between SO(2) and ODR was assumed and a linear model derived. After calibration, reproducibility and sensitivity were calculated in terms of SO(2). Systemic hyperoxia (n = 16) was induced in healthy volunteers by changing the O(2) concentration in inhaled air from 21% to 100%. RESULTS The automatic software enhanced reproducibility, and the mean SD for repeated measurements was 3.7% for arterioles and 5.3% venules, in terms of percentage of SO(2) (five repeats, 10 individuals). The model derived for calibration was SO(2) = 125 - 142 . ODR. The arterial SO(2) measured 96% +/- 9% (mean +/- SD) during normoxia and 101% +/- 8% during hyperoxia (n = 16). The difference between normoxia and hyperoxia was significant (P = 0.0027, paired t-test). Corresponding numbers for venules were 55% +/- 14% and 78% +/- 15% (P < 0.0001). SO(2) is displayed as a pseudocolor map drawn on fundus images. CONCLUSIONS The retinal oximeter is reliable, easy to use, and sensitive to changes in SO(2) when concentration of O(2) in inhaled air is changed.