Automatic Dry Eye Detection

Abstract

Abstract. Dry Eye Syndrome is a common disease in the western world, with effects from uncomfortable itchiness to permanent damage to the ocular surface. Nevertheless, there is still no objective test that provides reliable results. We have developed a new method for the automated detection of dry areas in videos taken after instilling fluorescein in the tear film. The method consists of a multi-step algorithm to first locate the iris in each image, then align the images and finally analyze the aligned sequence in order to find the regions of interest. Since the fluorescein spreads on the ocular surface of the eye the edges of the iris are fuzzy making the detection of the iris challenging. We use RANSAC to first detect the upper and lower eyelids and then the iris. Then we align the images by finding differences in intensities at different scales and using a least squares optimization method (Levenberg-Marquardt), to overcome the movement of the iris and the shaking of the camera. The method has been tested on videos taken from different patients. It is demonstrated to find the dry areas accurately and to provide a measure of the extent of the disease.

TMIS: a new image‐based software application for the measurement of tear meniscus height

“…A few studies have also focused on the automated detection of dry eye, including recognition of dry areas (Yedidya et al. , ), tear film lipid layer (García‐Resúa et al. ,b; Remeseiro et al.…”

A Comprehensive Survey on Image Modality based Computerized Dry Eye Disease Detection Techniques

“…The first automated DED detection technique was proposed in 2007 [15][16]. Input to this technique is an eye video that is captured using Eyescan device.…”

“…During the first step, the measurement areas are detected from the input video. The key benefit of this technique is that unlike [15,16], the measurement areas are detected automatically. They are detected based on symmetric difference in mean values of gray level of consecutive frames.…”

“…In this paper, we summarize them along with corresponding clinical tests. Based on our study, we classify the techniques into following three major categories subject to the approach used to identify the dry eye disease: 1) Techniques based on tear film stability [15][16][17][18][19][20][21][22][23][24][25], 2) Techniques based on blink rate detection [26] and 3) Techniques based on amount of tear produced [27]. DED detection techniques based on tear film stability further differ from each other with respect to imaging modality.…”

“…DED detection techniques based on tear film stability further differ from each other with respect to imaging modality. Hence, based on imaging modality used, we further classify them into three subcategories: techniques using fluorescein tear film video [15][16][17][18][19][20][21][22], techniques using lipid layer interference patterns [23][24] and techniques using thermal infrared images [25]. From our rigorous study on DED detection techniques, we have identified several parameters to evaluate them.…”