measurement of contact lens surface deposits using digital image processing

Journal of the British Contact Lens Association, Vol. 14, No. 4, pp 155-161, 1991 ©1991 British Contact Lens Association Printed in Great Britain

MEASUREMENT OF CONTACT LENS SURFACE DEPOSITS USING DIGITAL IMAGE PROCESSING

J im Gilchrist and Ian Hunter

KEY WORDS: Deposits, digitisation, measurement.

Introduction

T HE development and characteristics of contact lens surface deposits have been widely demon-

strated using a variety of techniques, including direct visual inspection with the lens on the eye 1, light-field microscopy 2, dark-field microscopy 8, and scanning electron microscopy. 4 However, although schemes have been proposed for grading the visibility of soft lens deposits under different viewing conditions 1,5 there are still no definitive methods by which the nature of lens deposits, or their density and distribution, can be accurately measured.

Three general approaches to measurement of lens deposits are outlined in Table 1.

Table 1. Measurement of lens deposits.

1. V I S U A L - QUALITATIVE Direct comparison of deposit area and density in two lenses of a pair. Degree of deposit area and density estimated in one of N categories.

2. VISUAL - QUANTITATIVE Estimates of actual percentage of lens surface occupied by deposit.

3. IMAGE A N A L Y S I S Any of the above approaches.

There are obvious limitations in grading schemes, based wholly on visual judgement. For example, stan- dardised viewing conditions and trained observers would be required to ensure consistency between laboratories and between different graders in the same laboratory. Furthermore, even trained observers would have difficulty in making the accurate quantitative estimates required if subtle changes in deposits are to be monitored. It is feasible to obtain more accurate visual estimates of the area of lens that is occupied by deposit, for example by projecting a photographic image of the lens with a superimposed grid through which the area of deposit may be coun- ted. However, the counting process is laborious and visually demanding, so the method is prone to errors

which would increase as the level of visual fatigue increases.

In situations where accurate assessment is required, particularly on large numbers of lenses, the use of digital image analysis offers the prospect of obtaining repeatable measurements of lens surface deposits automatically and relatively quickly. In addition, image enhancement techniques may be used, with or without measurement, to improve the visibility of lens deposits.

On the other hand, image analysis and interpretation techniques are still at a relatively early stage of development and there are difficulties in achieving robust solutions to real-world problems (Table 2). In this article, we explore the ability of selected general- purpose image analysis techniques to identify deposits of different types in dark-field contact lens images, and to provide estimates of the area that they occupy on the lens surface.

Table 2. Image analysis for deposit measurement.

Merits: Quantitative measurement of deposit area and density. Objective. Automatic. Repeatable.

Difficulties: Reliability depends on original image quality. Even 'clean' lens images may suffer from noise contamination.

The Nature of Contact Lens Deposits All image analysis systems, including human vision, use two general categories of information to identify features. These are the spectral and spatial differences that occur between different objects in any scene. Spectral information represents differences in colour and/or brightness, while spatial information represents differences in the form or structure of objects. Our first strategy for approaching contact lens analysis, therefore, is to establish the spectral and spatial nature of surface deposits.

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A number of authors have discussed the appearance of contact lens deposits. Generally, there are two categories: discrete, crystalline deposits and relatively uniform, film deposits. Both types of deposit are clearly revealed by dark-field microscopy ~, the imaging technique of choice for this study.

Discrete deposits are seen as small spots on the lens or, if large enough, as crystalline formations. These are generally inorganic in nature, most com- monly salts of calcium. In dark-field microscopy they appear as dense bright white spots. Film deposits begin as a wide distribution of very small granular spots which gradually coalesce over 2-3 months to form a continuous film. Films are organic in nature, consisting mainly of various proteins: lysozyme, albu- min, and lipoprotein and, sometimes, mucous which may produce a crazed appearance on the contact lens surface. Under dark-field illumination, film deposits appear as a pale haze against the darker regions of clear lens. 6

Table 3 summarises the types of lens deposit, show- ing that they belong to one of two classes with obvious spectral and spatial differences, and that they differ from regions of clear lens.

Table 3. Appearance of contact lens deposits (as seen in photographic negatives from dark-field microscopy).

Spectral Spatial Feature Class Characteristic Characteristic

Clear lens Light grey-white

Film deposit Mid-dark grey

Discrete deposit Dark grey-black

Large irregular regions

Medium-large irregular regions Small circular granules

Small-medium circular spots

Strategy for Contact Lens Image Analysis Our general approach to contact lens analysis is to exploit both the spatial and spectral differences between deposit types. We use spatial filtering techniques to produce two images that provide indepen- dent estimates of film and discrete deposit. Each of these filtered images is then segmented spectrally into two clases, clear lens and deposit, by statistical classification of every image pixel according to its grey level.

Figure I summarises the complete processing strategy. All image processing and display was carried out on high-resolution graphics workstations (SUN Sparcstation) using purpose-built interactive software written in C+ + and the X-Windows graphical user- interface standard.

Image Capture and Region-of-Interest Selection The image capture system consisted of a Wild Leitz Macroscope on which 35mm slides could be viewed and digitised via a JVC 870-N colour CCD camera linked to an image digitiser and framestore (Imaging Technology Inc, FG-100 series). Lens photographs were supplied in glass covered projection mounts to prevent damage to the photographic film. Each slide surface was cleaned before mounting in the slide holder, then digitised via the 'green' channel of the CCD camera to a 256 grey level image, with a resolution of 512 × 512 pixels. Colour cameras use three input channels (red, green, and blue) to encode colour information. Each channel captures a grey level image with different contrast and brightness characteristics, according to the spectral response of the channel. 7 The green channel was used in this study to give high contrast when capturing images from colour slides.

Original 256 grey-level Image captured from colour slide with spectral filtering to reduce background

ORIGINAL luminance variation - IMAGE 1, Image capture

2. Aspect ratio correction 3. Region Of Interest (Circle) mask set Interaetively 4. Area of clear lens Identified Interactlvely

Pre-processing~ Region of Interest: 1. Noise reduction 2. Contrast enhancement

PRE-PROCESSEn IMAGE

ESTIMATED DISCRETE ESTIMATED DEPOSIT FILM DEPOSIT

high frequency low frequency components components

Statistical classification of: 1. Clear lens 2. Discrete deposit 3. Film deposit

CLASSIFIED I- IMAGE I~

Figure 1. Contact lens image analysis strategy.

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The magnification of each image was arbitrary, and chosen so that the contact lens would almost fill the image frame. This is justified by the fact that all measurements of the extent of lens depbsit are taken as percentages of the whole lens area, rather than as absolute measures. Finally, a circular mask was set interactively on each lens image to specify the region- of-interest for analysis. This was adjusted to enclose as much of the lens area as possible. After capture and masking, the lens images were stored on disk (256kbytes per image) so that the later stages of processing and analysis could be performed contin- uously on a whole batch of images.

Pre-processing A fundamental principle in image analysis is that the quality of the final result depends on the quality of the original images. Image pre-processing involved two operations designed to improve image quality: smoothing to reduce unwanted background noise, and contrast enhancement to increase the spectral differences between features of interest.

Dark-field contact lens images (even those of appar- ently clean lenses) may exhibit a considerable amount of background 'noise'. This is generally of two types - physical, such as dust particles, air bubbles, and surface marks, and optical, such as luminance variation within the contact lens or distortion caused by the camera system. (An introduction to the subject of smoothing noisy images is given by Rosenfeld and Kak. s) The requirement is for a filter that offers a good noise reduction with minimal effect on true edges in the image. One of the simplest and most effective smoothing techniques is median filtering, by which the value of each image pixel is replaced by the median of all pixel values in a surrounding regi0n2.! ° A l l images in the present study were smoothed using a 5 × 5 pixel median filter.

Contrast enhancement involves mapping the given range of pixel grey levels into a larger range, so that dark levels become darker and bright levels brighter. This may be achieved using either linear or non-linear transformations, depending on whether it is desirable to preserve the relationship between levels across the range (linear) or to enhance features in one part of the range of the expense of others (non-linear). In this application we first adjusted the mean of the contact lens region for each image to fall at the centre of the grey level range, then applied a linear transformation to achieve maximum contrast. This ensured that the grey level distributions of each feature (clear lens, film, and discrete deposit), were widely spaced to aid segmentation, that the spectral relationships between the important features were preserved, and that the distributions of each feature would fall into a similar part of the range for each image in the data set. An example is given in Figure 2.

F i g u r e 2. Dark-field negative of a heavily deposited contact lens following image capture, circular masking, and pre- processing. Dark regions represent the deposit, and light regions the clear lens.

Spatial Filtering to Discriminate Deposit Type As described earlier, we chose to use spatial filtering to distinguish between film and discrete deposits. The principle employed here is that the spatial frequency components of image features differ. Spatial frequency represents the 'scale' at which luminance changes occur in the image. High frequencies correspond to many luminance changes within a given image region and are, therefore, associated with fine detail, such as discrete lens deposits. Medium and low spatial frequencies represent luminance changes that occur over larger areas, such as those produced by film deposits or background luminance variation. Image features that occupy certain parts of the spatial frequency range may be removed selectively by transfor- ming the image into the frequency domain (Fourier transform) and applying masks to remove specific bands of spatial frequencies. 11 We filtered the pre- processed lens images with a low-pass spatial frequency mask to estimate the film deposit component of the image, and with a high-pass mask to estimate the discrete deposit component. Figures 3 and 4 show the high- and low-pass filtered images, respectively.

Pixel Classification to Discriminate Between Clear Lens and Deposit The spatially filtered versions of each image were segmented into two regions using unsupervised statistical classification of grey level values. 12 This assumes that the image grey levels form two overlap- ping distributions (classes), which correspond to clear lens and deposit. In each image the deposit appears darker than the clear lens. The means and standard deviations of the two classes are first estimated using

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JIM GILCHRIST AND IAN HUNTER

Figure 3. High-pass spatial filtered version of Figure 2. Note that the spectral differences caused by f i lm deposit have been removed and the remaining dark areas represent discrete deposit.

Figure 5. Combined classified image from segmentation of Figures 3 and 4. Dark-grey areas represent f i lm deposits, mid-grey areas clear lens, and white areas discrete deposits.

Figure 4. Low-pass spatial filtered version of Figure 5. Discrete deposits have been removed in this case and the remaining dark areas represent f i lm deposit.

cluster analysis, then the grey level of each image pixel is examined and assigned to one of these two classes using maximum-likelihood classification. The technique is described as unsupervised because the class statistics are not provided in advance, but com- puted iteratively by the clustering algorithm. This enables the segmentation of each image to proceed automatically without guidance from the user. At this

Figure 6. Boundaries of the regions in Figure 8 are superimposed on the original image. Note the good agreement between actual discrete deposits and their boundaries as located by image analysis. The boundary line for f i lm deposits shows a poorer agreement with the visible appearance of the f i lm deposits. This is due to the very shallow luminance gradient between fi lm deposit and clear lens areas, which makes accurate statistical location of the boundary difficult.

stage the areas occupied by film and by discrete deposit may be easily calculated by counting the percentage of pixels in each class of the segmented images.

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After classification, the segmented versions of the spatially filtered images may be combined, as shown in Figure 5. Figure 6 shows the boundaries of the segmented deposits superimposed on the original image.

Evaluation of Image Analysis The analysis strategy described above was applied to two batches of contact lens images to evaluate its performance. Each batch was composed of the same 91 lenses photographed on different film types, and consisted of 76 lenses worn by patients (38 R & L pairs), and 15 known clean (i.e., unworn) lenses to provide a baseline reference for the absence of surface deposits. The lenses used were of three types; gas permeable (ZL9), low-water content soft (Z6), and high-water content soft (ES70).

Batch 1 images were photographed on XP1 monochrome negative film (ISO400), and Batch 2 on Kodak Ektachrome (Tso200) colour positive (slide) film. All the images of the set were subjected to exactly the same stages of capture, pre-processing, classification, and analysis as described above. The results obtained have been analysed only in relation to estimates of deposit area, since this indicates the accuracy of segmentation. Using the area data we assess:

$ The ability of the method to identify the clean lenses in each batch.

• The agreement of the image analysis estimates with visual judgements by three observers.

Identifying Known Clean Contact Lenses To assess the ability of image analysis to deal correctly with clean lenses, a number of known clean contact lenses were included in the set of lenses for analysis. We took the estimates of clear lens area obtained from all 91 lenses of each batch, and used cluster analysis to identify two groups; 'clean' (i.e., high percentage area of clear lens) and 'dirty' (low percentage area of clear lens). The rationale was that, with reliable segmentation, every known clean lens would give a high estimate of clear lens area and, therefore, be correctly classified as 'clean'. We found that 80% (12/15) clean lenses were correctly identified in Batch 1 (monochrome originals), whereas 93% (14/15) were correctly identified in Batch 2 (colour originals), as shown in Table 4.

Table 4. Image analysis evaluation: identification of clean contact lenses.

Film Accuracy (%)

Monochrome Without noise reduction filtering With noise reduction filtering

53 80

Colour With noise reduction filtering 93

Visual inspection of these images indicates that clean lens images tend to exhibit background luminance variations due to lens curvature and thickness, and that these may be misinterpreted as film deposit. The superior identification of clean lenses in images from colour slides is due to the fact that the background of clear lens appeared dark blue in the original tranparencies, so capture via the green input channel of the video camera reduced the background contribu- tion more than in the XP1 images, which carried no colour information.

Although it is an important test of the 'baseline' performance of the image analysis system if identification of clean lenses is required, the accuracy of clean lens identification is of limited significance for assessing the accuracy with which the system estimates known lens deposits. This is because the background luminance variation is only apparent in areas of clear lens. As deposits, especially film deposits, accumulate on the lens surface the underlying clear lens becomes progressively less visible, so the contri- bution of background luminance variation decreases as the area and density of surface deposit increases.

Estimation of Surface Deposit Area As outlined in the introduction, there are no established visual classification schemes for contact lens deposits that provide grading of the area of deposit on the lens surface. However, for this study we carried out visual estimates of lens deposits using two different strategies. Visual judgement of deposit type and percentage area. Two observers attempted direct judgement of the area of lens surface occupied by deposit. This was carried out by viewing a magnified image of each original lens colour slide on a binocular macroscope, classifying the lens according to the Rudko and Proby 5 scheme, and estimating the area occupied by each type of deposit. The estimates obtained were then combined to give a single percentage value for deposit and the value for each lens was assigned to one of four categories according to its magnitude, (0-25%, 25-50%, etc). Deposit area estimates for each lens by image analysis were categorised in the same w a y .

Visual judgement of differences between lenses of a pair. The major difficulty of direct judgement of deposit area as described above is again the lack of an established classification scheme that aids accurate magnitude estimates, for example by comparison against a standard. This means that direct estimates of deposit area taken in this manner are extremely variable between observers, and even within the same observer on different occasions. One way to provide a fixed reference for each visual judgement and to reduce variability is to present side-by-side images of contact lens pairs (R & L) from each patient and require observers simply to indicate which lens shows the higher (or lower) level of surface deposit.

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JIM GILCHRIST

Two observers took part in an experiment in which the paired images from each patient were displayed side-by-side on the computer screen, and observers had to classify the lenses according to which one showed the lower area of surface deposit. The observers were aware only that each lens in a pair belonged to the same patient, but did not know which

pat ient nor the R & L order of the lenses. Assessing the levels of deposit by assigning lenses

to categories enables the agreement between different observers and between visual and computer estimates to be assessed using the kappa coefficient. 1~ Kappa (~) provides a chance-corrected measure of agreement, so that for perfect agreement ~ = 1.0 and for chance agreement ~ = 0.0. Kappa coefficients for agreement between visual judgements and image analysis estimates are shown in Table 5.

Table 5. Image analysis evaluation: agreement with visual grading.

Method Kappa

Deposit estimated on four-step scale Observer 1 vs Observer 2 Observer 1 vs Observer 3 Observer 1 vs Image Analysis

Deposit judged by lens comparison (R & L) Observer 1 vs Observer 2 Observer 1 vs Image Analysis Observer 2 vs Image Analysis

0.30 0.33 0.37

0.62 0.84 0.62

The results show:

• Fair agreement between two observers for direct estimation of deposit area.

• Good agreement between two observers for comparison of deposit areas in lens pairs.

Given the high variability of direct magnitude esti- marion it is not surprising that agreement between observers is much poorer in this case. It is encourag- ing, however, that the agreement between image analysis and one human observer in both experiments is as good as the agreement between the two human observers used. This suggests that image analysis estimates are at least as reliable as visual estimates of deposit coverage.

Discussion and Conclusions This study represents the first known application of image analysis to the measurement of contact lens deposits. We have demonstrated that general-purpose image processing techniques enable two broad classes of lens deposit to be identified and measured, and that the methods used are at least as accurate as visual grading schemes in quantifying the area of lens

AND IAN H U N T E R

occupied by deposit. However, the technique must still be regarded as at an early stage of development and its reliability has not yet been satisfactorily demonstrated. In our view there are three fronts on which progress is required if an acceptable system for routine analysis is to be achieved.

Lens Image Quality For gross (i.e., whole lens) analysis, photography under dark-field illumination as used here is a cost- effective approach. Work is continuing in our laboratory to improve the quality of dark-field images by reducing optical system reflections, dust particles, air bubbles, etc. For future studies it will be feasible to capture full-colour digital lens images directly from a wet-cell on the macroscope stage, without the need for an intermediate photographic slide. This will remove a possible source of noise from the image capture stage and simplify the luminance calibration of the system, thus permitting a reliable measurement of deposit density.

An alternative approach in situations where the imaging of deposits at higher magnification is required may be the use of laser-scanning microscopy, which provides high-contrast, high-resolution images that are ideally suited to digital analysis.

Image Segmentation We have shown that identification of clean contact lenses may be hampered by background luminance variation within the lens. This could also lead to over- estimates of deposit for lightly deposited lenses. In later studies we therefore propose that full-colour analysis should be used to provide more spectral information to discriminate between clear lens and deposit. Other modifications to the segmentation pro- cedure are also planned to improve its performance. These include targetted removal of certain types of noise feature, e.g., dust tracks, at the pre-processing stage, improved strategies for spatial filtering and for pixel classification, which will take account of relationships between adjacent pixels.

Improved Deposit Grading Schemes The greatest difficulty in evaluating the performance of image analysis (or any new technique) for the measurement of lens deposits is the lack of a 'gold standard' against which to assess its reliability. Although they are intuitively appealing, visual judge- merits do not provide an acceptable standard for comparison. This is because they, in turn, cannot be applied according to any established or validated system of judgement. Also, even if such a system existed, the variability within and/or between human observers may exceed that of computer analysis. Future studies in this area, therefore, must seek a new specification for visual interpretation of the information in contact lens photographs and a gold standard scheme for measurement of lens deposits

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against which the performance of other methods may be evaluated. We are exploring the potential of using graphically generated simulations of the contact lens image as a basis for analysis. A successful simulation would overcome all of the gold standard problems, since the true characteristics of each simulated lens feature would be precisely known.

The methods described in this study represent a first stage and show promise for the eventual development of reliable image processing schemes for the analysis of contact lens deposits.

Acknowledgements We thank Stewart Mitchell, Tony Shakespeare, and Bill Douthwaite for helpful discussions and contact lens images, without which this work would have been impossible.

This research was funded by Angelini Pharmaceu- ticals, Roma, Italy.

Address for Correspondence Jim Gilchrist, Clinical Image Processing and Psychophysics (CLIPP) Laboratory, Department of Optometry, University of Bradford, West Yorkshire, United Kingdom.

R E F E R E N C E S

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3 Killpartrick, M.R. Soft lens contaminant detection by dark field illumination. Optician, 193, 34-37 (1987).

4 Prause, J.U., Brincker, P., Dreyer, V., and Vangsted, P. Light microscopical and scanning electron microscopical examinations of deposits on disposable constant wear lenses. Acta Ophthal- tool., 66, 3-8 (1988).

5 Rudko, P. and Proby, J. A Method for Classifying and Describ- ing Protein Deposition on the Hyd~vphilic Lens. Allergan Report Series, No 94 (1974).

6 Lowther, G.E. and Hilbert, J.A. Deposits on hydrophilic lenses: differential appearance and clinical causes. Am. J. Optom. Physiol. Optics., 52, 687-692 (1975).

7 Levine, M.D. Vision in Man and Machine, McGraw-Hill, New York, Section 2.3 (1985).

s Rosenfeld, A. and Kak, A.C. Digital Picture Processing, Academic Press, New York (1976).

9 Yang, G.J. and Huang, T.S. The effect of median filtering on edge location estimation. Comp. Graphics Image Proc., 15, 224-245 (1981).

10 Chin, R.T. and Yeh, C.L. Quantitative evaluation of some edge- preserving noise smoothing techniques. Comp. Vision, Graphics and Image Proc., 23, 67-91 (1983).

11 Gonzalez, R.C. and Wintz, P. Digital lmage Processing, Addison-Wesley Inc., Reading, MA (1977).

12 Duda, R.O. and Hart, P.E. Pattern Classification and Scene Analysis, John Wiley and Sons, New York (1973).

1~ Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas., 20 (1960).

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measurement of contact lens surface deposits using digital image processing

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