http://france.elsevier.com/direct/RBMRET/

ITBM-RBM 28 (2007) 35–41

Original article

Automatic identification of anterior segment eye abnormality

Identification automatique des anormalités du segment antérieur de l’œil

R. Acharya Ua,*, L.Y. Wongb, E.Y.K. Ngc, J.S. Surid,e

aDepartment of ECE, School of Engineering, Ngee Ann Polytechnic, 599489 Singapore, SingaporebDepartment of Electrical Engineering, National University of Singapore, Singapore, Singapore

c School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapored Idaho’s Biomedical Research Institute, ID, USA

eBiomedical Technologies Inc., CO, USA

Received 21 November 2006; accepted 26 February 2007Available online 09 April 2007

Abstract

The eyes are complex sensory organs and are designed to optimize vision under conditions of varying light. There are a number of eyedisorders that can influence vision. Eye disorders among the elderly are a major health problem. With advancing age, the normal function ofeye tissues decreases and there is an increased incidence of ocular pathology. The most common symptoms elicited from ocular diseases are fewin number and non-specific in nature: blurred vision, pain, and redness. Cataracts occur most frequently in older people and have significantimpact on an individual’s quality of life. There are effective therapies and visual aids for these potential vision-limiting conditions. Corneal hazea complication of refractive surgery is characterized by the cloudiness of the normally clear cornea. Iridocyclitis is the inflammation of the Irisand ciliary body. In corneal arcus are white circles in the cornea of the eye caused by fatty deposits. So, there is a need to diagnose to the normaleye from the abnormal one. This paper presents an identification of normal eye image and abnormal (consists of five kinds of eye images) classesusing radial basis function (RBF) classifier. The features are extracted from the raw images using the image processing techniques and fuzzy K-means algorithm. Our system uses 150 subjects, consisting of five different kinds of eye disease conditions. We demonstrated a sensitivity of90%, for the classifier with the specificity of 100%. Our systems are ready clinically to run on large amount of data sets.© 2007 Elsevier Masson SAS. All rights reserved.

Résumé

Les yeux sont des organes sensoriels complexes conçus pour optimiser la vision en ambiance lumineuse variable. De nombreuses pathologiesde l’œil peuvent détériorer la vision et les détecter chez les personnes âgées est un problème de santé majeur. Avec l’allongement de l’âge, lesfonctions normales des tissus oculaires déclinent avec une fréquence accrue des pathologies. Les symptômes les plus couramment découvertssont peu nombreux et de nature non spécifique : vision floue, douleur et rougeur. Les cataractes surviennent fréquemment chez les sujets âgés etont un impact considérable sur la qualité de vie. Des thérapies efficaces et des aides visuelles existent mais peuvent présenter des complications.La brume cornéenne est une complication chirurgicale caractérisée par l’aspect nuageux de la cornée normalement claire. L’iridocyclite est uneinflammation de l’iris et du corps ciliaire. Dans l’arc cornéen, des cercles blancs correspondent à des dépôts gras sur la cornée. Il existe donc unbesoin important de diagnostic pour différencier l’œil pathologique de l’œil normal. Ce papier présente une méthode de classification automa-tique d’image d’œil anormal en classes utilisant un classifieur à fonction de base radiale (RBF). Les traits caractéristiques sont extraits desdonnées brutes en utilisant des techniques de traitement d’images et un algorithme des K-means flou. Notre système a été testé sur 150 sujetsprésentant cinq types différents de pathologie oculaire. Nous avons observé une sensibilité du classifieur de 90 % et une spécificité de 100 %.Notre système est maintenant prêt à être utilisé en routine clinique sur un grand nombre de sujets.© 2007 Elsevier Masson SAS. All rights reserved.

* Corresponding author.E-mail address: aru@np.edu.sg (R. Acharya U).

1297-9562/$ - see front matter © 2007 Elsevier Masson SAS. All rights reserved.doi:10.1016/j.rbmret.2007.02.002

R. Acharya U et al. / ITBM-RBM 28 (2007) 35–4136

Keywords: Iridocyclitis; Cataract; Corneal haze; Corneal arcus; Eye; Sensitivity; Specificity

Mots clés : Iridocyclite ; Cataracte ; Brume cornéenne ; Arc cornéen ; Œil ; Sensibilité ; Spécificité

1. Introduction

Vision impairment is one of the most feared disabilities.Although it is believed that half of all blindness can be pre-vented, the number of people in the world, who suffer visionloss, continues to increase.

More than 1 million Americans 40 and over are blind fromeye disease and an additional 2.3 million are visually impaired[9]. Cataracts are the cause of nearly 50% of blindness world-wide. Each year more than 280,000 people in the United Stateshave problems with uveitis, which is inflammation of the mid-dle layer of the eye and a potentially blinding eye problem. Itcauses 30,000 new cases of blindness a year and up to 10% ofall the cases of blindness. It is more common in women andmore likely to occur in older people [10].

1.1. Normal eye

The normal eye [14,7,8,19,12] is made up of the sclera,cornea, pupil, aqueous humor, iris, conjunctiva, lens, vitreoushumor, ciliary body, macula, retina, fovea and the optic nerveas shown in Fig. 1.

Cornea is the clear outer part of the eye’s focusing systemlocated at the front of the eye. Most of the bending of the lightrays (refraction) occurs at the cornea. The lens also bends thelight but to a lesser extent. The lens does a sort of fine-tuningto insure that the image is sharply focused on the retina.

Pupil is the opening at the center of the iris. The iris adjuststhe size of the pupil and controls the amount of light that canenter the eye. Iris is the colored tissue behind the cornea—color varies from pale blue to dark brown.

Lens is the clear part of the eye behind the iris that helps tofocus light on the retina. The lens helps to focus on both farand near objects so that they are perceived clearly and sharply.The ciliary muscle helps to change the shape of the lens. Thischanging of lens shape is called accommodation. It is said thatthe frontal diameter of the lens is 10 mm.

Fig. 1. Cross section of human eye with major parts.

1.2. Cataract

Cataract is clouding of the natural lens, the part of the eyeresponsible for focusing light and producing clear, sharpimages. For most people, cataracts are a natural result ofaging [14,7,8,19,12].

This cataract is the leading cause of visual loss amongadults 55 and older. Eye injuries, certain medications, and dis-eases such as diabetes and alcoholism have also been known tocause cataracts. There are three types of cataracts (a) nuclearcataract; (b) cortical cataract; (c) subcapsular cataract. In thiswork, we have used the nuclear cataract eye image for thestudy.

1.3. Iridocyclitis

It is the inflammation of the Iris and ciliary body. It is a typeof anterior uveitis, a condition in which the uvea of the eyesuffers inflammation [14,7,8,19,12].

1.4. Corneal haze

An opacification or cloudiness of the normally clear corneathat occurs typically after photorefractive keratectomy (PRK)and rarely after laser in situ keratomileusis (LASIK). Anybuild up of inflammatory infiltrates (white blood cells), extramoisture, scar tissue, or foreign substances (like drugs) cancause a clouding of the cornea [14,7,8,19,12].

1.5. Corneal arcus

These are white circles in the cornea of the eye caused byfatty deposits. They are extremely common in middle-aged andelderly people but can affect younger people as well. Otherconditions, similar to corneal arcus, can also be a reflectionof inherited high blood cholesterol. These include Xanthelas-mas, which are fatty deposits that often appear as spots aroundthe eye area.

Edwards et al. [5], have classified normal and three types ofcataract optical eye images based on their distance from theaverage profiles in Euclidean space. Their system is able toclassify the unknown class correctly to the tune of 98%. Thenonmydriatic fundus camera was used successfully, as an alter-native method for screening of visually significant cataract forwide population [6]. A system for sequential color video cap-ture and analysis of lens opacities was proposed [3]. A sensi-tive red–green–blue (RGB) camera is coupled to a 486 DX2/66IBM-compatible computer to obtain high-resolution images ofcataract subjects. The VF-14 questionnaire reliably evaluatedfunctional differences caused by different cataract morpholo-gies; these differences were underestimated when only visualacuity was measured [17]. Patients with posterior subcapsular

Table 1Range of age, gender and number of subjects in each group

Types Normal Cataract Iridocyclitis Cornealarcus

Corneal haze

Age 32 ± 8 58 ± 13 45 ± 12 70 ± 10 70 ± 10Gender 35 males and

20 females25 males and15 females

15 males and10 females

10 males andfive females

10 males andfive females

R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41 37

cataracts had increased functional impairment, indicating thatcataract surgical intervention is indicated at an earlier stage inthese patients.

The findings suggest that brimonidine can cause anterioruveitis as a late side effect [1]. The inflammation settles rapidlyon stopping the medication and on using topical corticosteroidsand recurs on rechallenge with brimonidine. Idiopathic recur-rent acute anterior uveitis (RAAU) is a common reason forattendance at ophthalmic casualty departments [11]. It hasbeen proven that, the psychological factors like stress is not atriggering factor in the recurrence of idiopathic acute anterioruveitis. Uveitis in patients with psoriasis may have distinguish-ing clinical features [4]. Further epidemiologic studies arerequired to determine the strength of association between psor-iasis without arthritis but with uveitis. Subjects with cornealerosions and hypopyon iridocyclitis associated with continuouswear of aphakic soft contact lenses were treated with cyclople-gia and patching without antibiotics or corticosteroids [15].These subjects were completely recovered. The Lens OpacitiesClassification System III (LOCS III) is an improved LOCSsystem for grading slit-lamp and retroillumination images ofage-related cataract [2].

It was found that minimally invasive radial keratotomy(mini-RK) enhancement after PRK induces central cornealhaze and reduces corneal integrity [13]. Deep lamellar kerato-plasty for refractory corneal haze after refractive surgery wasuseful in this eye. Song et al. [16] have proved that the topicaltranilast could reduce corneal haze by suppressing TGF-beta1expression in keratocytes after PRK. The layout of the paper isas follows: Section 2 presents the data acquisition process, pre-processing and extraction of the three features Section 3 of thepaper discusses the neural network classifiers used for the clas-sification. Section 4 presents the results of the system andfinally the paper concludes in Section 5.

2. Data acquisition

For the purpose of the present work, about 150 subjects—patients suffering from glaucoma, cataract, corneal arcus as

Fig. 2. Optical eye images (a) normal (b) cataract (c

Fig. 3. Proposed schem

well as those in normal health—have been studied. Thesedata were taken from the Kasturba Medical Hospital, Eye Cen-tre, Manipal, India. The number and details of subjects in eachgroup is shown in Table 1. Images were stored in 24-bit TIFFformat with image size of 128 × 128 pixels. Fig. 2 shows thetypical normal, cataract, iridocyclitis, corneal haze and cor-neal arcus optical images.

2.1. Preprocessing

Fig. 3 shows the proposed scheme for the classification. Sixcentroids are obtained using fuzzy K-means algorithm andthese red, blue and green values of the six centroids are fedto the radial basis function (RBF) for classification. This stepinvolves histogram equalization and fuzzy K-means algorithm.These steps are explained below.

2.1.1. Histogram equalizationHistogram equalization is similar to contrast stretching in

that it attempts to increase the dynamic range of the pixelvalues in an image. It employs a monotonic, non-linear map-ping which re-assigns the intensity values of pixels in the inputimage such that the output image contains a uniform distribu-tion of intensities (i.e. a flat histogram). This technique is usedin image comparison processes (because it is effective in detailenhancement) and in the correction of non-linear effects intro-duced by, say, a digitizer or display system. The algorithmgiven in [18,21], deals with continuous tone images.

Histogram equalization is a process by which an image,which has very low contrast (signified by a grouping of largepeaks in a small area on the image’s histogram) can be

) iridocyclitis (d) corneal haze (e) corneal arcus.

e of classification.

R. Acharya U et al. / ITBM-RBM 28 (2007) 35–4138

enhanced to bring out details not previously visible. The histo-gram is just like a probability density function (PDF), and theidea behind histogram equalization is to get this PDF as closeto uniform as possible. Transfer function for histogram equal-ization is proportional to the cumulative histogram. The abovedescribes histogram equalization on a grayscale image. How-ever it can also be used on color images by applying the samemethod separately to the red, green and blue components of theRGB color values of the image.

Fig. 4. Probabilistic neural network architecture.

2.1.2. K-means algorithm

A self-organizing map is trained to classify the imagesbetween ‘normal’ and ‘diseased’ types. But before that, K-means clustering is used to group regions within each imageusing the RGB vector as an input. The K-means clusteringtechnique is as follows:

Let zjðkÞ ¼Rkj

Gkj

Bkj

0B@

1CA; where j ¼ 1; 2; …; 6 and x ¼

Rnn

Gnn

Bnn

0@

1A

R, G, B is the red, blue and green layers of the image, respec-tively. nn = any pixel in image.

Step 1: Choose six initial cluster centers: z1ð1Þ, z2ð1Þ, z3ð1Þ,z4ð1Þ, z5ð1Þ, z6ð1Þ. Six clusters were used because it bestvisually distinguishes the various parts on the captured imageof the anterior of the eye. The parts correspond to backgroundof the image, reflection, pupil, sclera and defects.

Step 2: At the kth iterative step, distribute samples {x}among six cluster domains, using the relation, k = 1, 2, …,total pixels on image:

x 2 SjðkÞ if ║x� zjðkÞ║ < ║x� ziðkÞ║ (1)

for all i = 1, 2, 3, …, 6, where i ≠ j, where SjðkÞ denotes the setof samples whose cluster center is zjðkÞ.

Step 3: From the results of step 2, compute the new clustercenters: zjðk þ 1Þ, j = 1, 2, 3, …, 6 such that the sum of thesquared distances from all points in SjðkÞ to the new clustercenter is minimized. The zjðk þ 1Þ minimizes the cluster centerto the mean samples in SjðkÞ. Therefore, the new cluster centeris given by:

zjðk þ 1Þ ¼ 1

Nj∑

x2SjðkÞx; j ¼ 1; 2; …; 6 (2)

where Nj is the number of samples in SjðkÞ. The name K-means is derived from the manner in which cluster centersare sequentially updated.

Step 4: If zjðk þ 1Þ ¼ zjðkÞ for j = 1, 2, …, 6, the algorithmhas converged and the procedure is terminated. Otherwise goto step 2.

After all images are processed using K-means clustering, thecluster centroids are fed into the RBF network for classifica-tion.

3. Classifier used

In this work, we have used RBF classifier for classification.The description of RBF is as follows.

3.1. RBF classifier

A neural network classifier is implemented using RBFs [20,22]. The net input to the radial basis transfer function is thevector distance between its weight vector w and the input vectorp, multiplied by the bias b. The RBF has a maximum of 1 whenits input is 0. As the distance between w and p decreases, theoutput increases. Thus a radial basis neuron acts as a detector,which produces 1 whenever the input p is identical to its weightvector w. Probabilistic neural network, which is a variant ofradial basis network is used for the classification purpose.When an input is presented, the first layer computes distancesfrom the input vector to the training input vectors and producesa vector whose element indicate how close the input is to atraining input. The second layer sums these contributions foreach class of inputs to produce as its net output vector probabil-ities. Finally, a complete transfer function on the output of thesecond layer picks the maximum of these probabilities and pro-duces a one for that class and a 0 for the other classes. Thearchitecture for this system is shown in Fig. 4.

In this implementation we have used D = 160 input trainingvector/target vector pairs. Each target vector has K = 3 elements.One of these elements is one and the rest is zero. Thus each inputvector is associated with one of K = 3 classes. The first layerinput weights w is set to the transpose of the matrix formedfrom the D training pairs. As the input feature vector has R = 6inputs, the weight matrix formed is of dimension 6 × D. When aninput x is presented, ║w� x║ is calculated. ║w� x║ indicateshow close the input is to the vectors of the training set. Theseelements are multiplied, element-by-element, by the bias and sentto the radial basis transfer function. An input vector close to atraining vector will be represented by a number close to one inthe output vector Q. The second layer weights p are set to thematrix T of target vectors. Each vector has a one only in the rowassociated with that particular class of input, and zeros elsewhere.

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The multiplication Qp sums the elements of Q due to each of theK input classes. Finally, the second layer transfer function iscomplete by finding producing a one corresponding to the largestelement and zeros elsewhere. Thus the network has classified theinput vector into a specific one of K classes because that classhad the maximum probability of being correct.

After all images are processed using K-means clustering, thecluster centroids are fed into a RBF network inputs to distin-guish the normal cornea from the other classes.

3.2. Statistical analysis

The image data is analyzed using the P-value obtainedusing analysis of variance (Anova between groups) test.Anova uses variances to decide whether the means are differ-

Fig. 5. Results of K-means clustering algorithm with six clusters for (a) norm

ent. This test uses the variation (variance) within the groupsand translates into variation (i.e. differences) between thegroups, taking into account how many subjects there are inthe groups. If the observed differences are high then it is con-sidered to be statistical significant.

4. Results

The K-means clustering algorithm is able to clearly distin-guish the parts of the eye image, as seen in Fig. 5. It can beseen from these figures that, the normal is different from therest of the eye classes. We obtained red, blue and green valuesfor each centroid. Hence, we have used 18 (six for red, greenand blue values) features for each image. The average of red,blue and green centroid is shown in Table 2. It shows the

al (b) corneal haze (c) cataract (d) corneal arcus (e) iridocyclitis classes.

Table 2Ranges of red, green and blue centroid (of all six clusters) for different eyeclasses

Type Red centroid Green centroid Blue centroid P-valuesNormal 114.97 ± 72.22 75.20 ± 63.63 36.92 ± 46.83 < 0.0001Corneal haze 107.08 ± 67.53 71.96 ± 55.64 43.42 ± 42.17 < 0.0001Corneal arcus 148.70 ± 57.25 120.49 ± 59.86 112.17 ± 65.67 < 0.0001Iridocyclitis 117.32 ± 69.80 76.77 ± 58.14 31.85 ± 38.73 < 0.0001Cataract 93.76 ± 63.56 60.86 ± 48.84 29.80 ± 32.45 < 0.0001

Table 3Number of training, testing data and percentage of classification

Type Number of dataused for training

Number of dataused for testing

Percentage (%) of correctclassificationRBF

Normal 1000 200 100Abnormal 1000 200 80Overall 2000 400 90

Table 4Result of sensitivity, specificity, and positive predictive accuracy for theclassifier

Classifier TN TP FP FN Sensitivity Specificity Positivepredictiveaccuracy

RBF 200 180 0 20 90 100 100

R. Acharya U et al. / ITBM-RBM 28 (2007) 35–4140

ranges of features used for the RBF for classification. Table 3shows the result of RBF classifier and the number of data usedfor training and testing.

During the training phase we have used 1000 data for nor-mal and abnormal cases. It is because, the neural networklearns better with huge and diverse data. During testingphase, 200 data is used to test for the classification efficiencyof the system. This result classifies all the normal eye imagescorrectly and about 80% of the abnormal eye classes correctly.Overall, our RBF neural network is able to classify an averageof 90% of the optical eye images correctly.

Fig. 6. Snapshot

The sensitivity of a test is the proportion of people with thedisease who have a positive test result. The higher the sensitiv-ity, the greater the detection rate and the lower the false nega-tive (FN) rate. The specificity of the test is the proportion ofpeople without the disease who have a negative test. Thehigher the specificity, the lower will be the false positive rateand the lower the proportion of people having the disease whowill be unnecessarily worried or exposed to unnecessary treat-ment. The positive predictive value of a test is the probabilityof a patient with a positive test actually having a disease. Thenegative predictive value is the probability of a patient with anegative test not having the disease.

The RBF neural network (NN) classifier is trained using sixcentroid inputs. The success on the trained network in classify-ing the images is shown above in Tables 3 and 4. It is seen thatthe network is 90% of the time accurate in identifying and clas-sifying the abnormal stages of the disease with 90% sensitivityand 100% specificity. Hence, it indicates that, these results areclinically significant. The accuracy of the system can further beincreased by increasing the size and quality of the training set.The classification results can be enhanced by extracting the stillbetter features from the optical images. The environmental con-ditions like the reflection of the light influences the quality of theoptical images and hence the percentage of classification effi-ciency. Also, the classification efficiency can be increased byremoving the background of the eye image before processingit; this includes removing the captured image of the eyelidalong with the eyelash so that only the eye is captured. Imagescaptured could be taken from the same distance. The angle ofthe image captured could be such that reflection is minimized.

The software for feature extraction and the program for clas-sification of eye images are written in MATLAB 7.0.4.

The snap shot of the graphical user interface (or GUI) of thesystem is shown in Fig. 6. Upload data button is provided to

of the GUI.

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R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41 41

load the input file. In our GUI shown, the input image isirid1.mat. Then the image corresponding to irid1.mat is dis-played in the area provided for Preview of the Input image.ANN Process button is provided to run the RBF neural networkto classify the input known data. The result of the classificationis displaced in Output text box. In the present case it is: Abnor-mal. Reset button is provided in remove the existing outputclass, before feeding the new input class to be classified. Theneural network used for this classification is shown in the cen-ter of the GUI and the typical normal, cataract, iridocyciltis,corneal haze and corneal arcus images are shown in the rightside of the GUI snap shot.

5. Conclusion

Eye diseases like cataract, iridocyclitis, corneal haze andcorneal arcus contribute cause of blindness and often cannotbe remedied because the patients are diagnosed too late withthe diseases.

In this paper, neural network classifiers are developed asdiagnostic tool to aid the physician in the detection of theseeye abnormalities. However, these tools generally do notyield results with 100% accuracy. The accuracy of the toolsdepend on several factors, such as the number of images usedand quality of the training set, the rigor of the trainingimparted, and also parameters chosen to represent the input.However, from the results listed in Tables 3 and 4, it is evidentthat the system is effective to the tune of about 90% accuracy.

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