CASDES: A Computer-Aided System to Support Dry Eye Diagnosis Based on Tear Film Maps.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2015.2419316, IEEE Journal of Biomedical and Health Informatics IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL . 1, NO. 1, JANUARY 2025

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CASDES: a computer-aided system to support dry eye diagnosis based on tear film maps Beatriz Remeseiro, Antonio Mosquera, Manuel G. Penedo

Abstract—Dry eye syndrome is recognized as a growing health problem, and one of the most frequent reasons for seeking eye care. Its etiology and management challenge clinicians and researchers alike, and several clinical tests can be used to diagnose it. One of the most frequently used tests is the evaluation of the interference patterns of the tear film lipid layer. Based on this clinical test, this paper presents CASDES, a computeraided system to support the diagnosis of dry eye syndrome. Furthermore, CASDES is also useful to support the diagnosis of other eye diseases, such as meibomian gland dysfunction, since it provides a tear film map with highly useful information for eye practitioners. Experiments demonstrate the robustness of this novel tool, which outperforms the previous attempts to create tear film maps and provides reliable results in comparison with the clinicians’ annotations. Note that the processing time is noticeably reduced with the proposed method, which will help to promote its clinical use in the diagnosis and treatment of dry eye. Index Terms—Dry eye, public health problem, computer-aided diagnosis, pattern recognition, image segmentation.

I. I NTRODUCTION Dry eye syndrome (DES) is a prevalent disease which leads to irritation of the ocular surface, and is associated with symptoms of discomfort and dryness [1]. Twenty-five percent of patients who visit ophthalmic clinics report symptoms of dry eye, making it a growing public health problem [2], [3], and one of the most common conditions seen by eye care practitioners [4]. Epidemiological studies identified its prevalence rates ranging from 7% to 33% depending on the concrete study, and the surveyed population [5]: over 14% of 65+ age group in one US study [6], and over 30% of the same age group in a population of Chinese subjects [7]. The current work conditions, such as computer use, has increased the proportion of people with DES [2]. Diagnosis of dry eye is a difficult task due to its multifactorial etiology, and several clinical tests can be used for both diagnosis and treatment. One of the most common tests is the evaluation of the interference patterns of the tear film lipid layer. In order to perform this test, Guillon designed the Tearscope Plus [8], an instrument which allows clinicians to Manuscript received June XX, 2020; revised June XX, 2020. This research has been partially funded by the Secretar´ıa de Estado de Investigación of the Spanish Government and FEDER funds of the European Union through the research project PI14/02161, and by the Conseller´ıa de Cultura, Educación e Ordenación Universitaria of the Xunta de Galicia through the research project GPC2013/065. Beatriz Remeseiro and Manuel G. Penedo are with the Departamento de Computación, Universidade da Coruña, Campus de Elviña s/n, A Coruña 15071, Spain. Antonio Mosquera is with the Departamento de Electrónica y Computación, Universidade de Santiago de Compostela, Campus Universitario Sur, Santiago de Compostela 15782, Spain

rapidly assess the lipid layer thickness. It projects a cylindrical source of cool white fluorescent light onto the lipid layer and, therefore, any observed phenomena is unique to its specific light source. Grading the lipid layer appearance should always be the first clinical observation to be made. For this reason, Guillon also defined a grading scale composed of five categories, which in increasing thickness are: open meshwork, closed meshwork, wave, amorphous, and color fringe. Note that the lipid layer thickness is associated with DES since a thinner lipid layer speeds up water evaporation. The classification into these five patterns is a difficult clinical task, especially with thinner lipid layers which lack color and/or morphological features. Furthermore, the subjective interpretation of experts via visual inspection may affect the classification, and so a high degree of inter- and also intraobserver variability can be produced [9]. A. Rationale of the approach Clinical overview: There is no doubt that the examination of the tear film lipid layer is a valuable, non-invasive technique which provides relevant information about the tear film, and so allows clinicians to diagnose dry eye syndrome despite its multifactorial etiology. Furthermore, the Tearscope Plus and the grading scale defined by Guillon have proven their validity to carry out this task [10], [11]. However, many eye care professionals have abandoned this clinical test because of the difficulty in interpreting the interference patterns and their classification into the grading scale. Related work: Some techniques have been designed to objectively measure the lipid layer thickness by means of a sophisticated optic system [12], or an interference camera which analyzes interference colors [13]. An automatic tool for tear film classification was initially proposed in [14], based on color and texture analysis, and machine learning algorithms. This tool was improved afterward to work in real-time [15], by means of feature selection techniques. The presence of multiple patterns (see Figure 1), which is a sign of meibomian gland abnormality [8], may affect the classification of a patient’s image into a single Guillon category. That is, the classifications provided by this automatic tool may be little reliable when multiple patterns appear. In this sense, first attempts to segment tear film images were proposed in [16], [17], with an accuracy over 85% and a processing time over an hour. To the best knowledge of the authors, there are no other attempts in the literature to tackle this problem. Regarding other clinical tests to diagnose DES, some attempts can be found in the literature: Ramos et al. [18]

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A. Location of the region of interest

(a)

(c)

(b)

(d)

(e)

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(e)

(b)

Fig. 1. Two representative images with heterogeneous tear film lipid layers, i.e. with multiple patterns: (a) wave, (b) closed meshwork, (c) amorphous, (d) color fringe, and (e) open meshwork.

proposed the automatic computation of the BUT test in tear film videos by identifying the blinks of the video and analyzing the evolution of their pixels in terms of intensity; Carpente et al. [19] presented a methodology for the semi-automatic computation of the NIBUT test by analyzing the stability or degradation of a special grid throughout the video frames; and Wu et al. [20] proposed a texture based segmentation approach for the detection of tear film breakup regions on interferometry images acquired with the Doane’s interferometer. However, as explained above, the grading lipid layer appearance should always be the first observation to be made. CASDES framework: It includes a novelty approach to create tear film maps, thus it can be used in clinical and research settings to improve the diagnosis and treatment of dry eye syndrome. CASDES makes three important contributions: (1) it provides a reliable distribution of interference patterns over the tear film, useful for dry eye diagnosis even in case of multiple patterns; (2) it outperforms previous approaches in terms of different performance measures, such as accuracy or precision; and (3) it noticeably reduces the processing time, which never before had been analyzed for tear film maps.

Input images acquired with the Tearscope Plus (see Figure 3(a)) include irrelevant areas, such as the sclera or the eyelids. The acquisition procedure guarantees that there is a central area in the image more illuminated than its surroundings, in which the tear film can be observed. Consequently, optometrists who visually analyze these images focus their attention on this part of the iris, i.e. the lightest area between the pupil and the boundary of the iris. This fact forces a preprocessing step, proposed in [17], to extract the region of interest (ROI) in which the analysis will take place. The tear film can be perceived with the highest contrast in the green channel of the input image, so only this channel will be considered in this step. The green channel is thresholded using its histogram, and then the normalized cross-correlation [21] is applied using circles as templates covering different pupil sizes. The circle with the maximum cross-correlation value corresponds to the pupil of the image. Next, a new circle with the same center than the previous one and a radius n times larger is created, and used as a first approach to the ROI. As explained above, the tear film can be observed as the light area between the pupil and the iris which surrounds it. Thus, a second approach to locate the ROI consists in finding those pixels whose gray level is under a global threshold: th = µ − p × σ

where µ is the mean value of the gray levels of the image, σ is its standard deviation, and p is a weight factor. Some images may include irrelevant regions, such as eyelashes or shadows cast by them. The morphological operator of erosion [22] is applied to eliminate them from this second approach. Finally, the logical AND operator between the two approaches is calculated, and a final adjustment is performed: the biggest circle concentric to the pupil is “divided” in 16 quadrants and, for each one, the minimum radius is considered to simplify the final ROI (see Figure 3(b)).

(a)

II. R ESEARCH METHODOLOGY CASDES is an automatic tool which, from an input image acquired with the Tearscope Plus, provides a labeled image known as tear film map that represents the distribution of the patterns over the tear film. To this end, five different steps are needed (see Figure 2): (1) locating the region of interest (ROI) in which the whole analysis will take place, (2) analyzing each window inside the ROI to obtain its color, texture features, (3) applying soft classification to compute the class-membership probabilities associated to the features, (4) applying the image segmentation process, and (5) post-processing the segmented image to finally obtain the tear film map.

(1)

(b)

Fig. 3. (a) Input image acquired with the Tearscope Plus. (b) Location of the region of interest over the input image.

B. Feature vector Once the ROI is located, the windows with a specified size [16] inside it are analyzed and a descriptor per window is obtained. This descriptor is a quantitative vector composed of features which represent color and texture properties. A wide set of popular color and texture models has been deeply analyzed in [23], whose results have motivated the use of a 23-feature descriptor obtained as follows:



3

Tearscope image

Tear film map

1. Location of the ROI Fig. 2.

2. Feature vector of each window

4. Image segmentation

3. Soft classification of each window

5. Post-processing

Five-step methodology of the CASDES framework

1) Color analysis. The CIE 1976 L*a*b* color space [24] is a chromatic color space which describes all the colors that the human eye can perceive. It is perceptually uniform, an important characteristic since clinicians’ perception is being imitated [23]. In this step, the input image is transformed from RGB to CIELAB, to subsequently analyze the texture of its three channels. 2) Texture analysis. The co-occurrence features technique [25] is used for texture extraction, since it is the most appropriate method for this problem [23]. It is an effective method which generates a set of gray level cooccurrence matrices, and extracts 14 statistical measures from them. The mean and range of these statistics are calculated across matrices to obtain a texture descriptor. 3) Feature selection. The correlation-based feature selection method [26] was used to reduce the number of features, and so the computational requirements. It is a filter algorithm which ranks feature subsets according to a correlation based function. An ad-hoc process based on it was used and the descriptor was reduced, from 588 to 23 features, with no degradation in performance [27]. C. Soft classification For each descriptor obtained from the windows located at the ROI, a support vector machine (SVM) [28] is used to compute its class-membership probabilities. The training process of the SVM is carried out using five classes, which correspond to the five lipid layer patterns. Once the SVM is trained, it is able to provide the five class-membership probabilities of a new descriptor by combining the pairwise class probabilities according to [29]. Notice that partial class-memberships are used in soft classification to model uncertain labeling and mixtures of classes. Therefore, the homogeneity criterion of the image segmentation algorithm subsequently presented will be based on these class-membership probabilities. Regarding the selection of the SVM as the machine learning algorithm, it was motivated by the results presented in [30], a research which includes an statistical analysis and concludes that the SVM is the most competitive method for the problem at hand in comparison with other popular classifiers. D. Image segmentation The well-known seeded region growing algorithm is considered in this step. Broadly speaking, it performs a segmentation of an image with respect to a set of initial points, known as seeds. Given the seeds, which can be manually or automatically selected, the algorithm finds a tessellation of the image

into regions. The idea is to analyze each connected component of seeds, through an iterative process, and perform the growing only if the components satisfy a homogeneity criterion. The original method was proposed to be applied on grayscale images [31]. In this paper, we propose a novel version of it to be applied using the class-membership probabilities provided by a soft classifier. The description of the new proposal is divided in two parts: the automatic search of the seeds, and the region growing from them. The seed search consists in analyzing all the windows inside the ROI to calculate their feature vectors and compute their class-membership probabilities. Based on this information, the center of the window will become a seed or not. Let i = 1..n be a class identifier where n is the number of classes, and CPsmax = max CPs [i] i

(2)

the maximum class-membership probability, where s is the center of the window ws ∈ ROI, and CPs [i] is the ith classmembership probability. Thus, we obtain the list of seeds L, where each seed s satisfies that ∀s|ws ∈ ROI, (s, i) ∈ L ↔ CPs [i] = CPsmax ≥ α

(3)

where α is the seed threshold. Next, the growing step is carried out to get the final regions based on the same information: the feature vectors of individual windows, and their class-membership probabilities. For this task, the matrix of regions R is initialized to 0, and a region per seed is created ∀(s, i) ∈ L, Bs = {s}

(4)

and labeled on R ∀(s, i) ∈ L, R[s] = i

(5)

Note that each region Bs ∈ R is initially composed of only the seed s, but then the growing step is carried out by analyzing its neighbors. Thus, Bs can be defined as a set of connected components with the same label i, using 8connectivity to define the concept of neighborhood. Following, all the neighbors of the seeds are added to SSL, a sorted list based on the homogeneity criterion defined as δe [Bs ] = |CPe [i] − µBs |

(6)

where e ∈ SSL is the center of the window we ∈ ROI, and



µ

Bs

=

P

CPe′ [i] |Bs |

e′ ∈Bs

(7)

The first element e is removed from the list, and its set of labeled neighbors N is analyzed in order to compute its value in R according to

4

Small regions may also appear in the map, and they usually correspond to false positives or noisy areas. Thus, this step also includes their elimination, and so the final tear film map is defined as: T Ff = {Bs ∈ T F, contour(Bs ) ≥ m}

(12)

where m is a minimum threshold (see Figure 4(d)). f (e) R[e] = −1

if ∀p, q ∈ N \ S, R[p] = R[q] otherwise

(8)

where −1 represents the boundary label, S = {p ∈ N, R[p] = −1}

(9)

and f (e) =

i if δe [Bs ] < β −1 otherwise

(10)

where β is the growing threshold. If the element e is labeled as i in R, then e is added to Bs , the value of µBs is recalculated, and all the unlabeled neighbors of e are added to SSL. Next, this iterative process is repeated until the sort list SSL does not contain any element. Finally, the matrix of regions is processed to obtain the tear film map defined as R[e] if R[e] > 0 T F [e] = (11) 0 otherwise Notice that T F is a labeled image in which each label is represented by a color associated to a Guillon category, and the label 0 corresponds to the background represented in black. Figure 4(b)) illustrates a tear film map obtained after the image segmentation step.

(a)

(b)

(c)

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Fig. 4. (a) Input image acquired with the Tearscope Plus and its region of study. Region of study after: (b) the image segmentation step, (c) the filling of holes, and (d) the elimination of small regions.

E. Post-processing After performing the image segmentation step, a tear film map T F is obtained. Its regions may contain small holes formed during the growing process. In order to homogenize the regions, each hole is “filled” in such a way that it will belong to the region which encloses it (see Figure 4(c)).

III. M ATERIALS AND METHODS The materials and methods used in this research are described in this section. A. Image acquisition The image acquisition was carried out with the Tearscope Plus (Keeler Ltd., UK) attached to a Topcon SL-D4 slit lamp (Topcon Medical Systems, USA). The interference patterns were observed through this slit-lamp microscope, with magnification set at 200X. The interference phenomena was recorded using a Topcon DV-3 digital video camera (Topcon Medical Systems, USA), and stored at a computer via the Topcon IMAGEnet i-base (Topcon Medical Systems, USA). The images have a spatial resolution of 1024 × 768 pixels. B. Image dataset The VOPTICAL R dataset [32] contains 50 images of the preocular tear film taken over optimum illumination conditions. These images were acquired from patients with ages ranging from 19 to 33 years. All images have been acquired and annotated by four experienced optometrists from the Optometry Service of the University of Santiago de Compostela (Spain), and the Physics Center of the University of Minho (Portugal). The annotations consist of delimited regions associated to the five Guillon categories. All procedures followed the Declaration of Helsinki, and the protocol was reviewed and approved by the Ethics Committee of the aforementioned University of Santiago de Compostela. C. Experimental procedure The experimental procedure is detailed as follows: 1) Train a SVM with radial kernel and automatic parameter estimation, using representative samples of the five patterns. Note that these samples were obtained from the VOPTICAL R dataset, and correspond to areas in which the four optometrists marked the same category. 2) Test the performance of CASDES by processing each image of the VOPTICAL R dataset: a) Locate its ROI. b) Apply the region growing algorithm using different values of the β parameter: calculate the feature vectors of the windows, and compute their classmembership probabilities using the trained SVM. c) Post-process the tear film map. d) Evaluate the tear film map using the annotations made by the four experienced optometrists. TM c Core i5 CPU Experimentation was performed on an Intel 760 @ 2.80GHz with RAM 16 GB.


This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2015.2419316, IEEE Journal of Biomedical and Health Informatics IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL . 1, NO. 1, JANUARY 2025 100

D. Configuration of parameters

IV. R ESULTS AND DISCUSSION The results provided by CASDES are following presented. A. CASDES vs. experts Figure 5 illustrates five representative images of the VOPTICAL R dataset annotated by four eye experts, and the tear film map provided by CASDES. If the regions marked by the experts are compared, it can be seen that they agree in some areas but they disagree in other ones. The same fact can be appreciated if the tear film maps are analyzed, since some of their regions match with the experts’ areas and others do not. Before quantifying the comparison between CASDES and the experts, it should be highlighted the difficulty that the optometrists have when marking the regions by hand. Figure 6 illustrates the difficulty of this problem, and was obtained by analyzing all the optometrists’ annotations of the VOPTICAL R dataset. This graphic represents, for each Guillon category, the probability of, given a random pixel classified in this category for a random expert, the other optometrists have classified this pixel in the same category. As can be seen, the optometrists find it easier to categorize the color fringe pattern, since the four of them agree in about a 40% of the pixels marked. In contrast, they fully agree in about a 20% of the pixels associated to the wave pattern. Note that a similar level of agreement can be observed in the other three patterns. The validation of the CASDES framework was performed at a pixel level by overlapping the tear film maps and the areas manually marked by the four experts. Thus, there are five different levels of agreement (0, 1, 2, 3 and 4 experts). In any classification problem, the terms true positive (TP), true negative (TN), false positive (FP), and false negative (FN) compare the category predicted by the system with the actual category. True and false refer to if the prediction corresponds to the expectation, while positive and negative

0 experts

1 expert 2 experts

80

(%) pixels marked by an experts

The aforementioned experimental procedure was carried out with the next parameter configuration: • The location of the ROI was carried out with the parameters set as n = 7, p = 0.4, whilst for the erosion process a 23 × 23 ellipse was used as a kernel [17]. • The window size was set as 32 × 32 pixels [16], which is the window size that allows a precise segmentation and maintains the texture well-defined. As square windows are used, the four central pixels are considered as the window center in both search of seeds and growing steps. • The threshold for the search of seeds was set as α = 0.9. This value provides a whole set of seeds; i.e. a greater threshold does not detect some seeds, whilst a lower threshold generates a bigger set of seeds but which correspond to the same final regions –they do not improve performance, whilst increasing the complexity–. • The threshold used in the post-processing step was set as m = 80 pixels. The selection criterion was the minimum perimeter size of the regions marked by the optometrists in the VOPTICAL R dataset [32].

5

3 experts

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40

20

0

CO

AM

WA

CM

OM

lipid layer patterns

Fig. 6. Reference graphic which illustrates the difficulty that experts have when marking the regions by hand. The bar plot represents the probability of, given a random pixel classified in a given category for a random expert, the other three optometrists (green), two of them (yellow), or just one of them (orange) have been classified this pixel in the same category.

refer to the prediction. These basic concepts have to be adapted to the problem at hand. Thus, positive and negative refer to if the system predicts a Guillon category or the background, respectively. Regarding true and false, the concepts are clear using 4 or 0 experts but the problem lies in the intermediate levels of agreement. Taking into account the difficulty of the problem illustrated in Figure 6, it seems reasonable that the agreement with at least 2 experts is equivalent to agreeing with 4 experts, whilst the agreement with 1 expert is equivalent to agreeing with none of them. Using this adapted terms, some popular performance measures [33] were calculated to estimate the performance of CASDES: • The accuracy is the proportion of true results, i.e. the percentage of correctly classified instances: Acc = •

TN + TP TP + FP + FN + TN

The precision is the proportion of the true positives against all the positive results: P rec =

•

TP TP + FP

(14)

The sensitivity measures the proportion of positives which are correctly classified: Sens =

•

(13)

TP TP + FN

(15)

The specificity measures the proportion of negatives which are correctly classified: Spec =

TN TN + FP

Figure 7 depicts the impact of the β parameter on the four performances measures considered. Note that the greater the β parameter, the higher the regions provided by the methodology since the homogeneity criterion of the growing step is less restrictive (see Equation 10). That is, the greater the β, the higher the number of positives, and the lower the number of negatives. As can be seen, the accuracy of the system is over 85% regardless of the β parameter but, in general, the higher



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Fig. 5. Sample images of the VOPTICAL R dataset. From left to right: the annotations made by the four optometrists, and tear film map provided by CASDES. Relation between colors and categories: red - open meshwork, yellow - closed meshwork, green - wave, cyan - amorphous, and blue - color fringe.

100

95

90

85

%

the β the lower the accuracy. The number of positives increases with β: for very low values of β, the number of TP is almost negligible –the regions are minimum, even only seeds–, and so there is a growing at the beginning of its tendency; then, a global maximum appears and from it on, the number of positives still increases but they are mostly FP which means a worse accuracy. The same tendency can be observed in the plots of both precision and specificity, although they do not reach the 85% and the 90%, respectively, in any case since they are being penalized by the agreement with only one expert. Regarding the sensitivity of CASDES, it is quite close to the 100% which means that the system rarely misclassifies those pixels associated to a Guillon category according to at least 2 experts. In this case, the tendency of the plot is completely different since the higher the β the higher the sensitivity, although the differences in this case are almost negligible. Summarizing, the sensitivity of the system remains almost stable with great values over 95% regardless the β parameter. However, the tendency of the other three metrics shows a noticeable decrease when the β increases. Additionally, each of these three plots presents a global maximum or peak for the same value β = 0.001, and so this β defines the most appropriate configuration for the problem at hand. Finally, these results are now compared to the ones provided

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Fig. 7. system

accuracy precision sensitivity specificity 0.005

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0.015

0.02

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parameter

0.035

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Impact of the β parameter on the performance of the CASDES

by the previous approach [17]. For the sake of brevity, only the performance measures of the best parameter configuration are illustrated in Table I. As can be seen, the accuracy of the previous approach does not surpass the 90% in any case, and even does not surpass the 75% when considering the wave pattern. Not only is the accuracy of the system improved when using CASDES, but also its precision and specificity due to the significant decrease in the number of false positives. In



contrast, the sensitivity is slightly lower when using the current approach, but with no significant differences. Therefore, the CASDES system presented in this paper provides more reliable results according to the experts’ criteria. TABLE I P ERFORMANCE MEASURES : ( A ) THE CASDES SYSTEM , ( B ) THE PREVIOUS APPROACH [17].

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the current approach implies a reduction in the processing time of almost a 70% regardless of the β value, which can surpass the 90% in the best case but at the expense of a worse performance. When using the best configuration in terms of performance, the reduction in time is over 85%. Therefore, with this configuration, CASDES is also able to noticeably outperform previous approach in terms of response time. 22

Prec 95.33 90.96 62.68 73.53 95.21 83.54

Sens 98.03 98.07 98.26 97.78 97.60 97.95

Spec 95.45 91.57 72.60 78.79 95.33 86.75

Color fringe Amorphous Wave Closed meshwork Open meshwork Average

(b) Acc 89.93 88.12 74.24 79.57 87.41 83.85

Prec 80.39 77.33 48.90 59.67 75.90 68.44

Sens 99.34 98.62 99.13 99.12 98.59 98.96

Spec 83.53 81.35 66.08 71.15 80.41 76.51

20 18 16

time (min)

Color fringe Amorphous Wave Closed meshwork Open meshwork Average

(a) Acc 96.71 94.58 80.78 85.93 96.43 90.89

14 12 10 8

B. Processing time The previous approach [17] processes all the windows inside the ROI and, although the feature extraction time over a single window is almost negligible (under 1 second), analyzing all the windows takes too long (over one hour). Thus, one of the targets of this research is to avoid this exhaustive processing and reduce the time. To this end, not all the windows inside the ROI are analyzed in the automatic search of seeds: only those windows separated by at least half of the window size. Experimentation has demonstrated that the number and location of the seeds obtained in such a way is enough in order to correctly segment the final regions. Hence, analyzing more windows increases the number of seeds, and so the time, with no improvement in performance. Regarding the growing process, only the neighbors of an existing region are analyzed, avoiding the analysis of all the windows inside the ROI. On the other hand, the center of each window has been defined as its four central pixels. Consequently, the regions do not grow pixel by pixel, and so the growing process is completed in a lower number of iterations, i.e. in a lower time. Figure 8 illustrates the impact of the β threshold in the processing time. As expected, the lower the β, the lower the time since the homogeneity criterion used in the growing step is more restrictive. That is, a lower β means that a lower number of neighbors are analyzed according to Equation 10, and so a lower processing time is needed to compute the final tear film map. Table II shows a comparative of the times needed to generate a tear film map with the previous and current approaches. Regarding the CASDES framework, three different values of β have been considered: the minimum and maximum values used in the experimentation; and the best configuration β = 0.001 according to the four performances measures previously analyzed. As can be seen, the use of

6 4 2

0

0.005

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0.015

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0.025

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0.035

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β parameter

Fig. 8. Impact of the β parameter on the time needed to create a tear film map using the CASDES system

TABLE II AVERAGE TIME NEEDED TO CREATE A TEAR FILM MAP

Previous approach [17] β = 0.00001 CASDES β = 0.001 β = 0.05

Time (min) 65.96 3.69 8.68 21.10

V. C ONCLUSIONS Dry eye syndrome is an important public health problem, which can be diagnosed by the observation of the tear film lipid layer patterns. This paper presents CASDES, a computeraided diagnosis system able to create tear film maps which illustrates the distribution of these patterns over the tear film. CASDES uses color and texture features, soft classification to model uncertainty, and a new, adapted version of the seeded region growing algorithm. The proposed methodology is able to generate tear film maps really similar to the regions marked by practitioners, with a high level of agreement between it and four experienced optometrists. In fact, it noticeably improves previous results in terms of three of the four performance measures considered with an accuracy over 90% on average, and with only a slight decrease in the sensitivity which is over 97%. Additionally, both specificity and precision are also improved with percentages over 86% and 83%, respectively. Furthermore, it outperforms the previous approach with a significant reduction in the processing time, which decreases over the 85% (from more than 60 minutes to less than 10 minutes). This improvement in terms of time is another point in favor the use of this novel system in a clinical routine. Therefore, CASDES is most likely to be applicable at any medical center to support



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