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Automatic Detection of Pre-Ocular Tear Film Break-Up Sequence in Dry Eyes

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Automatic Detection of Pre-Ocular Tear Film Break-Up Sequence in Dry Eyes Automatic Detection of Pre-Ocular Tear Film Break-Up Sequence in Dry Eyes Tamir Yedidya and Richard Hartley The Australian National University and National ICT Australia ¤ ftamir.yedidya, [email protected] Jean-Pierre Guillon Faculty of Medicine and Health Sciences, Lions Eye Institute, Australia [email protected] Abstract spread the fluorescein evenly, the tear ¯lm is viewed with the help of a yellow ¯lter in front of a slit-lamp Dry Eye Syndrome is a common disease in the west- biomicroscope. When a dark area appears, the lack ern world, with e®ects from uncomfortable itchiness to of fluorescence represents the rupture of the tear ¯lm permanent damage to the ocular surface. Neverthe- and the time elapsed since the last blink is recorded less, there is still no objective test that provides reli- as break-up time (BUT). The shorter the BUT, the able results. We extend our method [13] for the auto- more likely the diagnosis of dry eye [12]. The degree mated detection of dryness signs to include the break of blackness is related to the depth of the breakup. up time(BUT), analysis of the degree of thinning of The deeper the break, the greater the chances of ocular the tear ¯lm and detection of meniscus induced dry- surface damage. If the eyes are kept open, the area of ness, the last two have not been previously addressed. the break will increase in size and breaks may appear Our motivation is to help the clinician to automati- in new areas over the cornea. This is the test of tear cally detect and analyze various signs related to dry ¯lm stability most commonly used by clinicians as it is eyes. The method has been tested on over 100 videos minimally invasive [2]. taken from 30 di®erent patients. When compared to an After a break has happened, it is important to fur- analysis done by a specialized optometrist, our method ther analyze the break area. The location and depth is demonstrated to provide an accurate estimation for of the original and successive breaks give clinical in- the BUT and the extent of the disease. dications of the cause of the break and are helpful in determining what treatment to choose [1]. This is not possible with the current clinical routines due to the 1 Introduction subjectiveness of the grading methods. With the de- velopment of the Eye-Scan system [13], an easy to oper- ate, multi purpose system of video recording of anterior One of the roles of the tear ¯lm is the maintenance ocular structures is available. of corneal epithelial integrity and transparency which The exposed tear volume is divided into the pre- is achieved by keeping the ocular surface continuously ocular part, a thin layer covering the cornea and a moist. The pre-ocular tear ¯lm in humans does not marginal part, called lid tear meniscus or tear reser- remain stable for long periods of time [6]. When blink- voir and is situated along the upper and lower lids. ing is prevented, the tear ¯lm ruptures and dry spots The most critical part of the tear ¯lm is the junction appear over the cornea [7]. between the meniscus and the pre-ocular tear. At that The Fluorescein Break Up Time (FBUT) test was point, the surface tension forces make the formation of designed by Norn [9]. A moistened fluorescein strip is a continuous ¯lm nearly impossible [5]. The result is a applied to the conjunctiva and after a few blinks to line of minimal thickness often referred to as the black line because of its appearance when fluorescein is in- ¤National ICT Australia is funded by the Australian Govern- ment's Backing Australia's Ability initiative, in part through the stilled in the eye. That junction of minimal thickness Australia Research Council corresponds to the zone of tear ¯lm greatest instability. 1 ² Improved detection of the iris, when strong edges and noise are found on the conjunctiva (see Fig. 1(b)). ² Adapting the function which calculates the degree of thinning to handle large di®erences in illumina- tion between videos and parts of the same image. ² Analyzing dryness to include the area and location (a) (b) of the break. Figure 1. Samples of eye images after instilling fluorescein. (a) Immediately after a blink (b) just 2 Algorithm before the next blink. The darker areas are the dry eye areas which form through the sequence. They The dry areas always appear as darker areas in the are pointed out by arrows. The darker the area is, fluorescent image, however because of changing light the drier it became. One sees that dryness forms conditions and the amount of fluorescein that has been at di®erent locations with varying severity in the instilled, it is insu±cient simply to use some kind of iris area. The upper and lower bright curves are threshold on the last frame. Moreover, in order to the tear menisci. ¯nd the BUT, the whole video has to be scanned to pin-point the exact time when a certain area became dry. Also the camera is hand held resulting in move- Automatic methods for ¯nding the dry eye areas ment and change of scale of the iris. To overcome these are sparse in the current literature. However, there are problems, our algorithm is based on three main steps: some existing approaches for locating the iris as part 1) Detection of the iris and eyelids in the ¯rst frame 2) of other application mainly for iris recognition. Usu- Alignment of the iris in the rest of the sequence and 3) ally, assuming its shape is a perfect circle, the meth- Scanning the aligned video to ¯nd the dry areas and ods mostly use circle ¯tting algorithms to ¯rst locate BUT. the pupil and then the iris. Daugman [3] focused on iris recognition, and ¯nds the pupil-iris and iris-sclera 2.1 Detection of the iris and eyelids borders by searching over all circles in di®erent radii for the ones that gives the maximum contour integral The images of interest in the video1 are those be- derivative. Ma et al [8] also focus on iris recognition tween consecutive blinks. To that end, we ¯rst ¯nd all and ¯rst locate the darker pupil and then the iris using the blinks and half-blinks and treat each sequence indi- Canny edge detector and Hough transform. However, vidually. Blinks are detected when consecutive images Hough transform tends to be very slow and needs a few have big di®erences in intensity. Given an image I(x; y) iterations to ¯nd the correct radius. Ritter and el [10] after a blink, we create an edge map E(x; y) of the im- detect the pupil-iris border in slit lamp images in order age using the Canny edge detector. We found that to register the images. Their method is based on active Canny produces superior results to the Sobel edge de- contours that iteratively grow to ¯t to the iris border. tector that was used before, mainly due to the smooth- The acting forces use di®erences in intensity between ing and the removal of noisy disconnected areas, see the iris and the pupil and aim for a perfect polygon. Figs. 2(a&b). Then we create 3 threshold images: of However, as seen in Fig. 1, the pupil is not visible the iris, Iiris(x; y) = (E(x; y) > T1), and the lower in our videos and the boundaries between the iris and and upper eyelids, Ilow(x; y) = (E(x; y) > T2) and the sclera are fuzzy due to the fluorescein spreading. Iup(x; y) = (E(x; y) > T3). We use T1 · T2 · T3 Therefore, di®erent solutions are needed. as the edges of the eyelids are usually stronger than In this paper, we extend our previous work [13] in the iris's borders, which are fuzzy due to the fluores- several ways: cein spreading. The parameters for the Canny have been learnt using a subset of the total videos. All ² Detection of the break up time (BUT). videos were taken using the Eye-Scan camera, so the ² Separate detection of meniscus related dryness amount of light emitted and the angle of view were (black line). 1The captured videos are of resolution 352x288. The expected range of radii for the iris is between 90 to 130 pixels for most ² Improved detection of the eyelids. patients. The pixels above and below the upper and lower eyelids respectively in Iiris are removed, in order to ¯t a circle to the remaining pixels. We use RANSAC with three parameters (x; y; r) and at each iteration sample three points. Similar ideas to the eyelids ¯tting are used, imposing restrictions over the points distribution and limiting the radius to the expected range of iris's radii. After the iris has been detected, we use Levenberg- (a) (b) Marquardt(LM) [4] to do the ¯ne tuning by iteratively minimizing a non-linear function for ¯tting a circle. The initial estimation (x0; y0; r0) is the one found by the RANSAC. The main idea is to minimize the sum of distances of pixels with strong magnitudes to the circle (see more details in [13]). In order to choose as many pixels as possible that are actually on the iris, we add the following improvements: (c) (d) 1. The pixels with strongest gradients used for the LM minimization will not always fall only on the Figure 2. Results for ¯tting the iris and eyelids: iris, as the eyelids and textures on the sclera have Threshold image using (a) Sobel (b) Canny.
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