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Automatic identification of anterior segment eye abnormality
ITBM-RBM 28 (2007) 35–41 http://france.elsevier.com/direct/RBMRET/
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 a
Department of ECE, School of Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore b Department of Electrical Engineering, National University of Singapore, Singapore, Singapore c School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore d Idaho’s Biomedical Research Institute, ID, USA e Biomedical Technologies Inc., CO, USA Received 21 November 2006; accepted 26 February 2007 Available 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 eye disorders that can influence vision. Eye disorders among the elderly are a major health problem. With advancing age, the normal function of eye tissues decreases and there is an increased incidence of ocular pathology. The most common symptoms elicited from ocular diseases are few in number and non-specific in nature: blurred vision, pain, and redness. Cataracts occur most frequently in older people and have significant impact on an individual’s quality of life. There are effective therapies and visual aids for these potential vision-limiting conditions. Corneal haze a complication of refractive surgery is characterized by the cloudiness of the normally clear cornea. Iridocyclitis is the inflammation of the Iris and 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 normal eye from the abnormal one. This paper presents an identification of normal eye image and abnormal (consists of five kinds of eye images) classes using radial basis function (RBF) classifier. The features are extracted from the raw images using the image processing techniques and fuzzy Kmeans algorithm. Our system uses 150 subjects, consisting of five different kinds of eye disease conditions. We demonstrated a sensitivity of 90%, 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 pathologies de 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, les fonctions normales des tissus oculaires déclinent avec une fréquence accrue des pathologies. Les symptômes les plus couramment découverts sont peu nombreux et de nature non spécifique : vision floue, douleur et rougeur. Les cataractes surviennent fréquemment chez les sujets âgés et ont 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 une inflammation 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 un besoin important de diagnostic pour différencier l’œil pathologique de l’œil normal. Ce papier présente une méthode de classification automatique d’image d’œil anormal en classes utilisant un classifieur à fonction de base radiale (RBF). Les traits caractéristiques sont extraits des données brutes en utilisant des techniques de traitement d’images et un algorithme des K-means flou. Notre système a été testé sur 150 sujets présentant 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: [email protected] (R. Acharya U).
1297-9562/$ - see front matter © 2007 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.rbmret.2007.02.002
36
R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41
Keywords: Iridocyclitis; Cataract; Corneal haze; Corneal arcus; Eye; Sensitivity; Specificity Mots clés : Iridocyclite ; Cataracte ; Brume cornéenne ; Arc cornéen ; Œil ; Sensibilité ; Spécificité
1. Introduction
1.2. Cataract
Vision impairment is one of the most feared disabilities. Although it is believed that half of all blindness can be prevented, the number of people in the world, who suffer vision loss, continues to increase. More than 1 million Americans 40 and over are blind from eye disease and an additional 2.3 million are visually impaired [9]. Cataracts are the cause of nearly 50% of blindness worldwide. Each year more than 280,000 people in the United States have problems with uveitis, which is inflammation of the middle layer of the eye and a potentially blinding eye problem. It causes 30,000 new cases of blindness a year and up to 10% of all the cases of blindness. It is more common in women and more likely to occur in older people [10].
Cataract is clouding of the natural lens, the part of the eye responsible for focusing light and producing clear, sharp images. For most people, cataracts are a natural result of aging [14,7,8,19,12]. This cataract is the leading cause of visual loss among adults 55 and older. Eye injuries, certain medications, and diseases such as diabetes and alcoholism have also been known to cause cataracts. There are three types of cataracts (a) nuclear cataract; (b) cortical cataract; (c) subcapsular cataract. In this work, we have used the nuclear cataract eye image for the study.
1.1. Normal eye
It is the inflammation of the Iris and ciliary body. It is a type of anterior uveitis, a condition in which the uvea of the eye suffers inflammation [14,7,8,19,12].
The normal eye [14,7,8,19,12] is made up of the sclera, cornea, pupil, aqueous humor, iris, conjunctiva, lens, vitreous humor, ciliary body, macula, retina, fovea and the optic nerve as shown in Fig. 1. Cornea is the clear outer part of the eye’s focusing system located at the front of the eye. Most of the bending of the light rays (refraction) occurs at the cornea. The lens also bends the light but to a lesser extent. The lens does a sort of fine-tuning to insure that the image is sharply focused on the retina. Pupil is the opening at the center of the iris. The iris adjusts the size of the pupil and controls the amount of light that can enter 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 to focus light on the retina. The lens helps to focus on both far and near objects so that they are perceived clearly and sharply. The ciliary muscle helps to change the shape of the lens. This changing of lens shape is called accommodation. It is said that the frontal diameter of the lens is 10 mm.
Fig. 1. Cross section of human eye with major parts.
1.3. Iridocyclitis
1.4. Corneal haze An opacification or cloudiness of the normally clear cornea that occurs typically after photorefractive keratectomy (PRK) and rarely after laser in situ keratomileusis (LASIK). Any build up of inflammatory infiltrates (white blood cells), extra moisture, scar tissue, or foreign substances (like drugs) can cause 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 by fatty deposits. They are extremely common in middle-aged and elderly people but can affect younger people as well. Other conditions, similar to corneal arcus, can also be a reflection of inherited high blood cholesterol. These include Xanthelasmas, which are fatty deposits that often appear as spots around the eye area. Edwards et al. [5], have classified normal and three types of cataract optical eye images based on their distance from the average profiles in Euclidean space. Their system is able to classify the unknown class correctly to the tune of 98%. The nonmydriatic fundus camera was used successfully, as an alternative method for screening of visually significant cataract for wide population [6]. A system for sequential color video capture and analysis of lens opacities was proposed [3]. A sensitive red–green–blue (RGB) camera is coupled to a 486 DX2/66 IBM-compatible computer to obtain high-resolution images of cataract subjects. The VF-14 questionnaire reliably evaluated functional differences caused by different cataract morphologies; these differences were underestimated when only visual acuity was measured [17]. Patients with posterior subcapsular
R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41
cataracts had increased functional impairment, indicating that cataract surgical intervention is indicated at an earlier stage in these patients. The findings suggest that brimonidine can cause anterior uveitis as a late side effect [1]. The inflammation settles rapidly on stopping the medication and on using topical corticosteroids and recurs on rechallenge with brimonidine. Idiopathic recurrent acute anterior uveitis (RAAU) is a common reason for attendance at ophthalmic casualty departments [11]. It has been proven that, the psychological factors like stress is not a triggering factor in the recurrence of idiopathic acute anterior uveitis. Uveitis in patients with psoriasis may have distinguishing clinical features [4]. Further epidemiologic studies are required to determine the strength of association between psoriasis without arthritis but with uveitis. Subjects with corneal erosions and hypopyon iridocyclitis associated with continuous wear of aphakic soft contact lenses were treated with cycloplegia and patching without antibiotics or corticosteroids [15]. These subjects were completely recovered. The Lens Opacities Classification System III (LOCS III) is an improved LOCS system for grading slit-lamp and retroillumination images of age-related cataract [2]. It was found that minimally invasive radial keratotomy (mini-RK) enhancement after PRK induces central corneal haze and reduces corneal integrity [13]. Deep lamellar keratoplasty for refractory corneal haze after refractive surgery was useful in this eye. Song et al. [16] have proved that the topical tranilast could reduce corneal haze by suppressing TGF-beta1 expression in keratocytes after PRK. The layout of the paper is as follows: Section 2 presents the data acquisition process, preprocessing and extraction of the three features Section 3 of the paper discusses the neural network classifiers used for the classification. Section 4 presents the results of the system and finally 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
37
Table 1 Range of age, gender and number of subjects in each group Types
Normal
Cataract
Iridocyclitis Corneal arcus Age 32 ± 8 58 ± 13 45 ± 12 70 ± 10 Gender 35 males and 25 males and 15 males and 10 males and 20 females 15 females 10 females five females
Corneal haze 70 ± 10 10 males and five females
well as those in normal health—have been studied. These data were taken from the Kasturba Medical Hospital, Eye Centre, Manipal, India. The number and details of subjects in each group is shown in Table 1. Images were stored in 24-bit TIFF format with image size of 128 × 128 pixels. Fig. 2 shows the typical normal, cataract, iridocyclitis, corneal haze and corneal arcus optical images. 2.1. Preprocessing Fig. 3 shows the proposed scheme for the classification. Six centroids are obtained using fuzzy K-means algorithm and these red, blue and green values of the six centroids are fed to the radial basis function (RBF) for classification. This step involves histogram equalization and fuzzy K-means algorithm. These steps are explained below. 2.1.1. Histogram equalization Histogram equalization is similar to contrast stretching in that it attempts to increase the dynamic range of the pixel values in an image. It employs a monotonic, non-linear mapping which re-assigns the intensity values of pixels in the input image such that the output image contains a uniform distribution of intensities (i.e. a flat histogram). This technique is used in image comparison processes (because it is effective in detail enhancement) and in the correction of non-linear effects introduced by, say, a digitizer or display system. The algorithm given 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 large peaks in a small area on the image’s histogram) can be
Fig. 2. Optical eye images (a) normal (b) cataract (c) iridocyclitis (d) corneal haze (e) corneal arcus.
Fig. 3. Proposed scheme of classification.
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R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41
enhanced to bring out details not previously visible. The histogram is just like a probability density function (PDF), and the idea behind histogram equalization is to get this PDF as close to uniform as possible. Transfer function for histogram equalization is proportional to the cumulative histogram. The above describes histogram equalization on a grayscale image. However it can also be used on color images by applying the same method separately to the red, green and blue components of the RGB color values of the image. 2.1.2. K-means algorithm A self-organizing map is trained to classify the images between ‘normal’ and ‘diseased’ types. But before that, Kmeans clustering is used to group regions within each image using the RGB vector as an input. The K-means clustering technique is as follows: 0 k1 0 nn 1 Rj R B C Let zj ðkÞ ¼ @ Gkj A; where j ¼ 1; 2; …; 6 and x ¼ @ Gnn A Bnn Bkj R, G, B is the red, blue and green layers of the image, respectively. nn = any pixel in image. Step 1: Choose six initial cluster centers: z1 ð1Þ, z2 ð1Þ, z3 ð1Þ, z4 ð1Þ, z5 ð1Þ, z6 ð1Þ. Six clusters were used because it best visually distinguishes the various parts on the captured image of the anterior of the eye. The parts correspond to background of 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 S j ðkÞ if ║x zj ðkÞ║ < ║x zi ðkÞ║
(1)
for all i = 1, 2, 3, …, 6, where i ≠ j, where S j ðkÞ denotes the set of samples whose cluster center is zj ðkÞ. Step 3: From the results of step 2, compute the new cluster centers: zj ðk þ 1Þ, j = 1, 2, 3, …, 6 such that the sum of the squared distances from all points in S j ðkÞ to the new cluster center is minimized. The zj ðk þ 1Þ minimizes the cluster center to the mean samples in S j ðkÞ. Therefore, the new cluster center is given by: zj ðk þ 1Þ ¼
1 ∑ x; j ¼ 1; 2; …; 6 N j x2S j ðkÞ
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 the vector distance between its weight vector w and the input vector p, multiplied by the bias b. The RBF has a maximum of 1 when its input is 0. As the distance between w and p decreases, the output increases. Thus a radial basis neuron acts as a detector, which produces 1 whenever the input p is identical to its weight vector w. Probabilistic neural network, which is a variant of radial basis network is used for the classification purpose. When an input is presented, the first layer computes distances from the input vector to the training input vectors and produces a vector whose element indicate how close the input is to a training input. The second layer sums these contributions for each class of inputs to produce as its net output vector probabilities. Finally, a complete transfer function on the output of the second layer picks the maximum of these probabilities and produces a one for that class and a 0 for the other classes. The architecture for this system is shown in Fig. 4. In this implementation we have used D = 160 input training vector/target vector pairs. Each target vector has K = 3 elements. One of these elements is one and the rest is zero. Thus each input vector is associated with one of K = 3 classes. The first layer input weights w is set to the transpose of the matrix formed from the D training pairs. As the input feature vector has R = 6 inputs, the weight matrix formed is of dimension 6 × D. When an input x is presented, ║w x║ is calculated. ║w x║ indicates how close the input is to the vectors of the training set. These elements are multiplied, element-by-element, by the bias and sent to the radial basis transfer function. An input vector close to a training vector will be represented by a number close to one in the output vector Q. The second layer weights p are set to the matrix T of target vectors. Each vector has a one only in the row associated with that particular class of input, and zeros elsewhere.
(2)
where N j is the number of samples in S j ðkÞ. The name Kmeans is derived from the manner in which cluster centers are sequentially updated. Step 4: If zj ðk þ 1Þ ¼ zj ðkÞ for j = 1, 2, …, 6, the algorithm has converged and the procedure is terminated. Otherwise go to step 2. After all images are processed using K-means clustering, the cluster centroids are fed into the RBF network for classification.
Fig. 4. Probabilistic neural network architecture.
R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41
The multiplication Qp sums the elements of Q due to each of the K input classes. Finally, the second layer transfer function is complete by finding producing a one corresponding to the largest element and zeros elsewhere. Thus the network has classified the input vector into a specific one of K classes because that class had the maximum probability of being correct. After all images are processed using K-means clustering, the cluster centroids are fed into a RBF network inputs to distinguish the normal cornea from the other classes. 3.2. Statistical analysis The image data is analyzed using the P-value obtained using analysis of variance (Anova between groups) test. Anova uses variances to decide whether the means are differ-
39
ent. This test uses the variation (variance) within the groups and translates into variation (i.e. differences) between the groups, taking into account how many subjects there are in the groups. If the observed differences are high then it is considered to be statistical significant. 4. Results The K-means clustering algorithm is able to clearly distinguish the parts of the eye image, as seen in Fig. 5. It can be seen from these figures that, the normal is different from the rest of the eye classes. We obtained red, blue and green values for each centroid. Hence, we have used 18 (six for red, green and blue values) features for each image. The average of red, blue and green centroid is shown in Table 2. It shows the
Fig. 5. Results of K-means clustering algorithm with six clusters for (a) normal (b) corneal haze (c) cataract (d) corneal arcus (e) iridocyclitis classes.
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R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41
Table 2 Ranges of red, green and blue centroid (of all six clusters) for different eye classes Type Normal Corneal haze Corneal arcus Iridocyclitis Cataract
Red centroid 114.97 ± 72.22 107.08 ± 67.53 148.70 ± 57.25 117.32 ± 69.80 93.76 ± 63.56
Green centroid 75.20 ± 63.63 71.96 ± 55.64 120.49 ± 59.86 76.77 ± 58.14 60.86 ± 48.84
Blue centroid 36.92 ± 46.83 43.42 ± 42.17 112.17 ± 65.67 31.85 ± 38.73 29.80 ± 32.45
P-values < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
Table 3 Number of training, testing data and percentage of classification Type
Number of data used for training
Number of data used for testing
Normal Abnormal Overall
1000 1000 2000
200 200 400
Percentage (%) of correct classification RBF 100 80 90
Table 4 Result of sensitivity, specificity, and positive predictive accuracy for the classifier Classifier
TN
TP
FP
FN
Sensitivity
Specificity
RBF
200
180
0
20
90
100
Positive predictive accuracy 100
ranges of features used for the RBF for classification. Table 3 shows the result of RBF classifier and the number of data used for training and testing. During the training phase we have used 1000 data for normal and abnormal cases. It is because, the neural network learns better with huge and diverse data. During testing phase, 200 data is used to test for the classification efficiency of the system. This result classifies all the normal eye images correctly and about 80% of the abnormal eye classes correctly. Overall, our RBF neural network is able to classify an average of 90% of the optical eye images correctly.
The sensitivity of a test is the proportion of people with the disease who have a positive test result. The higher the sensitivity, the greater the detection rate and the lower the false negative (FN) rate. The specificity of the test is the proportion of people without the disease who have a negative test. The higher the specificity, the lower will be the false positive rate and the lower the proportion of people having the disease who will be unnecessarily worried or exposed to unnecessary treatment. The positive predictive value of a test is the probability of a patient with a positive test actually having a disease. The negative predictive value is the probability of a patient with a negative test not having the disease. The RBF neural network (NN) classifier is trained using six centroid inputs. The success on the trained network in classifying the images is shown above in Tables 3 and 4. It is seen that the network is 90% of the time accurate in identifying and classifying the abnormal stages of the disease with 90% sensitivity and 100% specificity. Hence, it indicates that, these results are clinically significant. The accuracy of the system can further be increased by increasing the size and quality of the training set. The classification results can be enhanced by extracting the still better features from the optical images. The environmental conditions like the reflection of the light influences the quality of the optical images and hence the percentage of classification efficiency. Also, the classification efficiency can be increased by removing the background of the eye image before processing it; this includes removing the captured image of the eyelid along with the eyelash so that only the eye is captured. Images captured could be taken from the same distance. The angle of the image captured could be such that reflection is minimized. The software for feature extraction and the program for classification of eye images are written in MATLAB 7.0.4. The snap shot of the graphical user interface (or GUI) of the system is shown in Fig. 6. Upload data button is provided to
Fig. 6. Snapshot of the GUI.
R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41
load the input file. In our GUI shown, the input image is irid1.mat. Then the image corresponding to irid1.mat is displayed in the area provided for Preview of the Input image. ANN Process button is provided to run the RBF neural network to classify the input known data. The result of the classification is displaced in Output text box. In the present case it is: Abnormal. Reset button is provided in remove the existing output class, before feeding the new input class to be classified. The neural network used for this classification is shown in the center of the GUI and the typical normal, cataract, iridocyciltis, corneal haze and corneal arcus images are shown in the right side of the GUI snap shot. 5. Conclusion Eye diseases like cataract, iridocyclitis, corneal haze and corneal arcus contribute cause of blindness and often cannot be remedied because the patients are diagnosed too late with the diseases. In this paper, neural network classifiers are developed as diagnostic tool to aid the physician in the detection of these eye abnormalities. However, these tools generally do not yield results with 100% accuracy. The accuracy of the tools depend on several factors, such as the number of images used and quality of the training set, the rigor of the training imparted, and also parameters chosen to represent the input. However, from the results listed in Tables 3 and 4, it is evident that the system is effective to the tune of about 90% accuracy. References [1] [2]
Byles DB, Frith P, Salmon JF. Anterior uveitis as a side effect of topical brimonidine. Am J Ophthalmol 2000;130(3):287–91. Chylack LT, Wolfe JK, Singer DM, Leske MC, Bullimore MA, Bailey IL, et al. The Lens Opacities Classification System III. The Longitudinal Study of Cataract Study Group. Arch Ophthalmol 1993;111(6): 831–6.
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[3] Cotlier E. Cataract image analysis system. Eye 1999;13(Pt 3b):457–63. [4] Durrani K, Foster CS. Psoriatic uveitis: a distinct clinical entity? Am J Ophthalmol 2005;139(1):106–11. [5] Edwards PA, Datiles MB, Unser M, Trus BL, Freidlin V, Kashima K. Computerized cataract detection and classification. Curr Eye Res 1990;9 (6):517–24. [6] Ferraro JG, Pollard T, Muller A, Lamoureux EL, Taylor HR. Detecting cataract causing visual impairment using a nonmydriatic fundus camera. Am J Ophthalmol 2005;139(4):725–6. [7] Finlay RD, Payne PAG. The eye in general practice. Spain: ButterworthHeinemann; 1998. [8] Fletcher R, Still DC. Eye examination and refraction. 2nd ed. Blackwell Science Ltd.; 1998. [9] http://www.nei.nih.gov/eyedata/pdf/VPUS.pdf. [10] http://www.allaboutvision.com/resources/statistics-eye-diseases.htm. [11] Mulholland B, Marks M, Lightman SL. Anterior uveitis and its relation to stress. Br J Ophthalmol 2000;84(10):1121–4. [12] Remington LA. Clinical anatomy of the visual system. USA: Butterworth-Heinemann; 1998. [13] Shoji N, Hayashi E, Shimizu K, Uga S, Sugita J. Central corneal haze increased by radial keratotomy following photorefractive keratectomy. J Refract Surg 2003;19(5):560–5. [14] Snell RS, Lemp MA. Clinical anatomy of the eye. 2nd ed. Blackwell Science Inc.; 1997. [15] Snyder DA, Litinsky SM, Gelender H. Hypopyon iridocyclitis associated with extended-wear soft contact lenses. Am J Ophthalmol 1982;93(4): 519–20. [16] Song JS, Jung HR, Kim HM. Effects of topical tranilast on corneal haze after photorefractive keratectomy. J Cataract Refract Surg 2005;31(5): 1065–73. [17] Stifter E, Sacu S, Weghaupt H. Functional vision with cataracts of different morphologies: comparative study. J Cataract Refract Surg 2004;30 (9):1883–91. [18] Tou JT, Gonzalez RC. Pattern recognition principles. Reading, MA: Addison-Wesley Publisher Co; 1974. [19] Vander JF, Gault JA. Ophthalmology secrets. 2nd ed. USA: Hanley & Belfus Inc.; 2002. [20] Wasserman PD. Advanced methods in neural computing. New York: Van Nostrand Reinhold; 1993. [21] Wintz P, Gonzalez R. Digitial image processing. 2nd ed. India: PrenticeHall; 1987. [22] Yagnanarayana B. Artificial neural networks. India: Prentice-Hall; 1999.
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 a
Department of ECE, School of Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore b Department of Electrical Engineering, National University of Singapore, Singapore, Singapore c School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore d Idaho’s Biomedical Research Institute, ID, USA e Biomedical Technologies Inc., CO, USA Received 21 November 2006; accepted 26 February 2007 Available 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 eye disorders that can influence vision. Eye disorders among the elderly are a major health problem. With advancing age, the normal function of eye tissues decreases and there is an increased incidence of ocular pathology. The most common symptoms elicited from ocular diseases are few in number and non-specific in nature: blurred vision, pain, and redness. Cataracts occur most frequently in older people and have significant impact on an individual’s quality of life. There are effective therapies and visual aids for these potential vision-limiting conditions. Corneal haze a complication of refractive surgery is characterized by the cloudiness of the normally clear cornea. Iridocyclitis is the inflammation of the Iris and 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 normal eye from the abnormal one. This paper presents an identification of normal eye image and abnormal (consists of five kinds of eye images) classes using radial basis function (RBF) classifier. The features are extracted from the raw images using the image processing techniques and fuzzy Kmeans algorithm. Our system uses 150 subjects, consisting of five different kinds of eye disease conditions. We demonstrated a sensitivity of 90%, 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 pathologies de 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, les fonctions normales des tissus oculaires déclinent avec une fréquence accrue des pathologies. Les symptômes les plus couramment découverts sont peu nombreux et de nature non spécifique : vision floue, douleur et rougeur. Les cataractes surviennent fréquemment chez les sujets âgés et ont 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 une inflammation 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 un besoin important de diagnostic pour différencier l’œil pathologique de l’œil normal. Ce papier présente une méthode de classification automatique d’image d’œil anormal en classes utilisant un classifieur à fonction de base radiale (RBF). Les traits caractéristiques sont extraits des données brutes en utilisant des techniques de traitement d’images et un algorithme des K-means flou. Notre système a été testé sur 150 sujets présentant 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: [email protected] (R. Acharya U).
1297-9562/$ - see front matter © 2007 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.rbmret.2007.02.002
36
R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41
Keywords: Iridocyclitis; Cataract; Corneal haze; Corneal arcus; Eye; Sensitivity; Specificity Mots clés : Iridocyclite ; Cataracte ; Brume cornéenne ; Arc cornéen ; Œil ; Sensibilité ; Spécificité
1. Introduction
1.2. Cataract
Vision impairment is one of the most feared disabilities. Although it is believed that half of all blindness can be prevented, the number of people in the world, who suffer vision loss, continues to increase. More than 1 million Americans 40 and over are blind from eye disease and an additional 2.3 million are visually impaired [9]. Cataracts are the cause of nearly 50% of blindness worldwide. Each year more than 280,000 people in the United States have problems with uveitis, which is inflammation of the middle layer of the eye and a potentially blinding eye problem. It causes 30,000 new cases of blindness a year and up to 10% of all the cases of blindness. It is more common in women and more likely to occur in older people [10].
Cataract is clouding of the natural lens, the part of the eye responsible for focusing light and producing clear, sharp images. For most people, cataracts are a natural result of aging [14,7,8,19,12]. This cataract is the leading cause of visual loss among adults 55 and older. Eye injuries, certain medications, and diseases such as diabetes and alcoholism have also been known to cause cataracts. There are three types of cataracts (a) nuclear cataract; (b) cortical cataract; (c) subcapsular cataract. In this work, we have used the nuclear cataract eye image for the study.
1.1. Normal eye
It is the inflammation of the Iris and ciliary body. It is a type of anterior uveitis, a condition in which the uvea of the eye suffers inflammation [14,7,8,19,12].
The normal eye [14,7,8,19,12] is made up of the sclera, cornea, pupil, aqueous humor, iris, conjunctiva, lens, vitreous humor, ciliary body, macula, retina, fovea and the optic nerve as shown in Fig. 1. Cornea is the clear outer part of the eye’s focusing system located at the front of the eye. Most of the bending of the light rays (refraction) occurs at the cornea. The lens also bends the light but to a lesser extent. The lens does a sort of fine-tuning to insure that the image is sharply focused on the retina. Pupil is the opening at the center of the iris. The iris adjusts the size of the pupil and controls the amount of light that can enter 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 to focus light on the retina. The lens helps to focus on both far and near objects so that they are perceived clearly and sharply. The ciliary muscle helps to change the shape of the lens. This changing of lens shape is called accommodation. It is said that the frontal diameter of the lens is 10 mm.
Fig. 1. Cross section of human eye with major parts.
1.3. Iridocyclitis
1.4. Corneal haze An opacification or cloudiness of the normally clear cornea that occurs typically after photorefractive keratectomy (PRK) and rarely after laser in situ keratomileusis (LASIK). Any build up of inflammatory infiltrates (white blood cells), extra moisture, scar tissue, or foreign substances (like drugs) can cause 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 by fatty deposits. They are extremely common in middle-aged and elderly people but can affect younger people as well. Other conditions, similar to corneal arcus, can also be a reflection of inherited high blood cholesterol. These include Xanthelasmas, which are fatty deposits that often appear as spots around the eye area. Edwards et al. [5], have classified normal and three types of cataract optical eye images based on their distance from the average profiles in Euclidean space. Their system is able to classify the unknown class correctly to the tune of 98%. The nonmydriatic fundus camera was used successfully, as an alternative method for screening of visually significant cataract for wide population [6]. A system for sequential color video capture and analysis of lens opacities was proposed [3]. A sensitive red–green–blue (RGB) camera is coupled to a 486 DX2/66 IBM-compatible computer to obtain high-resolution images of cataract subjects. The VF-14 questionnaire reliably evaluated functional differences caused by different cataract morphologies; these differences were underestimated when only visual acuity was measured [17]. Patients with posterior subcapsular
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cataracts had increased functional impairment, indicating that cataract surgical intervention is indicated at an earlier stage in these patients. The findings suggest that brimonidine can cause anterior uveitis as a late side effect [1]. The inflammation settles rapidly on stopping the medication and on using topical corticosteroids and recurs on rechallenge with brimonidine. Idiopathic recurrent acute anterior uveitis (RAAU) is a common reason for attendance at ophthalmic casualty departments [11]. It has been proven that, the psychological factors like stress is not a triggering factor in the recurrence of idiopathic acute anterior uveitis. Uveitis in patients with psoriasis may have distinguishing clinical features [4]. Further epidemiologic studies are required to determine the strength of association between psoriasis without arthritis but with uveitis. Subjects with corneal erosions and hypopyon iridocyclitis associated with continuous wear of aphakic soft contact lenses were treated with cycloplegia and patching without antibiotics or corticosteroids [15]. These subjects were completely recovered. The Lens Opacities Classification System III (LOCS III) is an improved LOCS system for grading slit-lamp and retroillumination images of age-related cataract [2]. It was found that minimally invasive radial keratotomy (mini-RK) enhancement after PRK induces central corneal haze and reduces corneal integrity [13]. Deep lamellar keratoplasty for refractory corneal haze after refractive surgery was useful in this eye. Song et al. [16] have proved that the topical tranilast could reduce corneal haze by suppressing TGF-beta1 expression in keratocytes after PRK. The layout of the paper is as follows: Section 2 presents the data acquisition process, preprocessing and extraction of the three features Section 3 of the paper discusses the neural network classifiers used for the classification. Section 4 presents the results of the system and finally 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
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Table 1 Range of age, gender and number of subjects in each group Types
Normal
Cataract
Iridocyclitis Corneal arcus Age 32 ± 8 58 ± 13 45 ± 12 70 ± 10 Gender 35 males and 25 males and 15 males and 10 males and 20 females 15 females 10 females five females
Corneal haze 70 ± 10 10 males and five females
well as those in normal health—have been studied. These data were taken from the Kasturba Medical Hospital, Eye Centre, Manipal, India. The number and details of subjects in each group is shown in Table 1. Images were stored in 24-bit TIFF format with image size of 128 × 128 pixels. Fig. 2 shows the typical normal, cataract, iridocyclitis, corneal haze and corneal arcus optical images. 2.1. Preprocessing Fig. 3 shows the proposed scheme for the classification. Six centroids are obtained using fuzzy K-means algorithm and these red, blue and green values of the six centroids are fed to the radial basis function (RBF) for classification. This step involves histogram equalization and fuzzy K-means algorithm. These steps are explained below. 2.1.1. Histogram equalization Histogram equalization is similar to contrast stretching in that it attempts to increase the dynamic range of the pixel values in an image. It employs a monotonic, non-linear mapping which re-assigns the intensity values of pixels in the input image such that the output image contains a uniform distribution of intensities (i.e. a flat histogram). This technique is used in image comparison processes (because it is effective in detail enhancement) and in the correction of non-linear effects introduced by, say, a digitizer or display system. The algorithm given 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 large peaks in a small area on the image’s histogram) can be
Fig. 2. Optical eye images (a) normal (b) cataract (c) iridocyclitis (d) corneal haze (e) corneal arcus.
Fig. 3. Proposed scheme of classification.
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enhanced to bring out details not previously visible. The histogram is just like a probability density function (PDF), and the idea behind histogram equalization is to get this PDF as close to uniform as possible. Transfer function for histogram equalization is proportional to the cumulative histogram. The above describes histogram equalization on a grayscale image. However it can also be used on color images by applying the same method separately to the red, green and blue components of the RGB color values of the image. 2.1.2. K-means algorithm A self-organizing map is trained to classify the images between ‘normal’ and ‘diseased’ types. But before that, Kmeans clustering is used to group regions within each image using the RGB vector as an input. The K-means clustering technique is as follows: 0 k1 0 nn 1 Rj R B C Let zj ðkÞ ¼ @ Gkj A; where j ¼ 1; 2; …; 6 and x ¼ @ Gnn A Bnn Bkj R, G, B is the red, blue and green layers of the image, respectively. nn = any pixel in image. Step 1: Choose six initial cluster centers: z1 ð1Þ, z2 ð1Þ, z3 ð1Þ, z4 ð1Þ, z5 ð1Þ, z6 ð1Þ. Six clusters were used because it best visually distinguishes the various parts on the captured image of the anterior of the eye. The parts correspond to background of 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 S j ðkÞ if ║x zj ðkÞ║ < ║x zi ðkÞ║
(1)
for all i = 1, 2, 3, …, 6, where i ≠ j, where S j ðkÞ denotes the set of samples whose cluster center is zj ðkÞ. Step 3: From the results of step 2, compute the new cluster centers: zj ðk þ 1Þ, j = 1, 2, 3, …, 6 such that the sum of the squared distances from all points in S j ðkÞ to the new cluster center is minimized. The zj ðk þ 1Þ minimizes the cluster center to the mean samples in S j ðkÞ. Therefore, the new cluster center is given by: zj ðk þ 1Þ ¼
1 ∑ x; j ¼ 1; 2; …; 6 N j x2S j ðkÞ
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 the vector distance between its weight vector w and the input vector p, multiplied by the bias b. The RBF has a maximum of 1 when its input is 0. As the distance between w and p decreases, the output increases. Thus a radial basis neuron acts as a detector, which produces 1 whenever the input p is identical to its weight vector w. Probabilistic neural network, which is a variant of radial basis network is used for the classification purpose. When an input is presented, the first layer computes distances from the input vector to the training input vectors and produces a vector whose element indicate how close the input is to a training input. The second layer sums these contributions for each class of inputs to produce as its net output vector probabilities. Finally, a complete transfer function on the output of the second layer picks the maximum of these probabilities and produces a one for that class and a 0 for the other classes. The architecture for this system is shown in Fig. 4. In this implementation we have used D = 160 input training vector/target vector pairs. Each target vector has K = 3 elements. One of these elements is one and the rest is zero. Thus each input vector is associated with one of K = 3 classes. The first layer input weights w is set to the transpose of the matrix formed from the D training pairs. As the input feature vector has R = 6 inputs, the weight matrix formed is of dimension 6 × D. When an input x is presented, ║w x║ is calculated. ║w x║ indicates how close the input is to the vectors of the training set. These elements are multiplied, element-by-element, by the bias and sent to the radial basis transfer function. An input vector close to a training vector will be represented by a number close to one in the output vector Q. The second layer weights p are set to the matrix T of target vectors. Each vector has a one only in the row associated with that particular class of input, and zeros elsewhere.
(2)
where N j is the number of samples in S j ðkÞ. The name Kmeans is derived from the manner in which cluster centers are sequentially updated. Step 4: If zj ðk þ 1Þ ¼ zj ðkÞ for j = 1, 2, …, 6, the algorithm has converged and the procedure is terminated. Otherwise go to step 2. After all images are processed using K-means clustering, the cluster centroids are fed into the RBF network for classification.
Fig. 4. Probabilistic neural network architecture.
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The multiplication Qp sums the elements of Q due to each of the K input classes. Finally, the second layer transfer function is complete by finding producing a one corresponding to the largest element and zeros elsewhere. Thus the network has classified the input vector into a specific one of K classes because that class had the maximum probability of being correct. After all images are processed using K-means clustering, the cluster centroids are fed into a RBF network inputs to distinguish the normal cornea from the other classes. 3.2. Statistical analysis The image data is analyzed using the P-value obtained using analysis of variance (Anova between groups) test. Anova uses variances to decide whether the means are differ-
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ent. This test uses the variation (variance) within the groups and translates into variation (i.e. differences) between the groups, taking into account how many subjects there are in the groups. If the observed differences are high then it is considered to be statistical significant. 4. Results The K-means clustering algorithm is able to clearly distinguish the parts of the eye image, as seen in Fig. 5. It can be seen from these figures that, the normal is different from the rest of the eye classes. We obtained red, blue and green values for each centroid. Hence, we have used 18 (six for red, green and blue values) features for each image. The average of red, blue and green centroid is shown in Table 2. It shows the
Fig. 5. Results of K-means clustering algorithm with six clusters for (a) normal (b) corneal haze (c) cataract (d) corneal arcus (e) iridocyclitis classes.
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Table 2 Ranges of red, green and blue centroid (of all six clusters) for different eye classes Type Normal Corneal haze Corneal arcus Iridocyclitis Cataract
Red centroid 114.97 ± 72.22 107.08 ± 67.53 148.70 ± 57.25 117.32 ± 69.80 93.76 ± 63.56
Green centroid 75.20 ± 63.63 71.96 ± 55.64 120.49 ± 59.86 76.77 ± 58.14 60.86 ± 48.84
Blue centroid 36.92 ± 46.83 43.42 ± 42.17 112.17 ± 65.67 31.85 ± 38.73 29.80 ± 32.45
P-values < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
Table 3 Number of training, testing data and percentage of classification Type
Number of data used for training
Number of data used for testing
Normal Abnormal Overall
1000 1000 2000
200 200 400
Percentage (%) of correct classification RBF 100 80 90
Table 4 Result of sensitivity, specificity, and positive predictive accuracy for the classifier Classifier
TN
TP
FP
FN
Sensitivity
Specificity
RBF
200
180
0
20
90
100
Positive predictive accuracy 100
ranges of features used for the RBF for classification. Table 3 shows the result of RBF classifier and the number of data used for training and testing. During the training phase we have used 1000 data for normal and abnormal cases. It is because, the neural network learns better with huge and diverse data. During testing phase, 200 data is used to test for the classification efficiency of the system. This result classifies all the normal eye images correctly and about 80% of the abnormal eye classes correctly. Overall, our RBF neural network is able to classify an average of 90% of the optical eye images correctly.
The sensitivity of a test is the proportion of people with the disease who have a positive test result. The higher the sensitivity, the greater the detection rate and the lower the false negative (FN) rate. The specificity of the test is the proportion of people without the disease who have a negative test. The higher the specificity, the lower will be the false positive rate and the lower the proportion of people having the disease who will be unnecessarily worried or exposed to unnecessary treatment. The positive predictive value of a test is the probability of a patient with a positive test actually having a disease. The negative predictive value is the probability of a patient with a negative test not having the disease. The RBF neural network (NN) classifier is trained using six centroid inputs. The success on the trained network in classifying the images is shown above in Tables 3 and 4. It is seen that the network is 90% of the time accurate in identifying and classifying the abnormal stages of the disease with 90% sensitivity and 100% specificity. Hence, it indicates that, these results are clinically significant. The accuracy of the system can further be increased by increasing the size and quality of the training set. The classification results can be enhanced by extracting the still better features from the optical images. The environmental conditions like the reflection of the light influences the quality of the optical images and hence the percentage of classification efficiency. Also, the classification efficiency can be increased by removing the background of the eye image before processing it; this includes removing the captured image of the eyelid along with the eyelash so that only the eye is captured. Images captured could be taken from the same distance. The angle of the image captured could be such that reflection is minimized. The software for feature extraction and the program for classification of eye images are written in MATLAB 7.0.4. The snap shot of the graphical user interface (or GUI) of the system is shown in Fig. 6. Upload data button is provided to
Fig. 6. Snapshot of the GUI.
R. Acharya U et al. / ITBM-RBM 28 (2007) 35–41
load the input file. In our GUI shown, the input image is irid1.mat. Then the image corresponding to irid1.mat is displayed in the area provided for Preview of the Input image. ANN Process button is provided to run the RBF neural network to classify the input known data. The result of the classification is displaced in Output text box. In the present case it is: Abnormal. Reset button is provided in remove the existing output class, before feeding the new input class to be classified. The neural network used for this classification is shown in the center of the GUI and the typical normal, cataract, iridocyciltis, corneal haze and corneal arcus images are shown in the right side of the GUI snap shot. 5. Conclusion Eye diseases like cataract, iridocyclitis, corneal haze and corneal arcus contribute cause of blindness and often cannot be remedied because the patients are diagnosed too late with the diseases. In this paper, neural network classifiers are developed as diagnostic tool to aid the physician in the detection of these eye abnormalities. However, these tools generally do not yield results with 100% accuracy. The accuracy of the tools depend on several factors, such as the number of images used and quality of the training set, the rigor of the training imparted, and also parameters chosen to represent the input. However, from the results listed in Tables 3 and 4, it is evident that the system is effective to the tune of about 90% accuracy. References [1] [2]
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