Automatic System for Retinal Disease Screening

Automatic System for Retinal Disease Screening Arathy.T College Of Engineering Karunagaally Abstract This work investigates discrimination caabilities in the texture of fundus images to differentiate between athological and healthy images. For this urose, the erformance of Local Binary Patterns (LBP) as a texture descritor for retinal images has been exlored.the goal is to distinguish between diabetic retinoathy (DR), age related macular degeneration (AMD),Glaucoma and normal fundus images analysing the texture of the retina background and avoiding a revious lesion segmentation stage. Glaucoma is a athological condition, rogressive neurodegeneration of the otic nerve, which causes vision loss. The damage to the otic nerve occurs due to the increase in ressure within the eye. Glaucoma is evaluated by monitoring intra ocular ressure (IOP), visual field and the otic disc aearance (cu -to-disc ratio). Cu-to disc ratio (CDR) is normally a time invariant feature. Therefore, it is one of the most acceted indicators of this disease and the disease rogression. In this aer,multi thresholding method with median filter is used to find the CDR from the color fundus images to determine athological rocess of glaucoma. The roosed technique able to categorize all the glaucoma disease images. Seven exeriments were conducted and validated with the roosed rocedure: AMD -Normal, DR - Normal, Glaucoma-Normal, and the severity of these diseases, and 4 class roblem (AMD - DR Glaucoma- Normal). Keywords Local Binary Patterns, Diabetic Retinoathy,Age-related Macular Degeneration, AMD, Glaucoma,Fundus Image, Retinal Image. I. INTRODUCTION The World Health Organization (WHO) estimates that in 2010 there were 285 million eole visually imaired around the world. In site of the fact that the number of blindness cases has been significantly reduced in recent years, it is estimated that 80% of the cases of visual imairment are reventable or treatable. Lots of eole in rural and semi-urban areas suffer from eye diseases such as Diabetic Retinoathy, Glaucoma, Age based Macular Degradation etc. Diabetic retinoathy (DR), Age - related macular degeneration (AMD) and Glaucoma are the most frequent causes of blindness and vision loss. In addition, these diseases will exerience a high growth in the future due to diabetes incidence increase, ageing oulation in the current society and high intraocular ressure. Diabetic retinoathy (DR) is a comlication of diabetes that can lead to imairment of vision and even blindness. It is the most common cause of blindness in the working-age oulation. DR is the main cause of new cases of blindness among adults aged 20 to 74 years. During the first 20 years of the disease, nearly all atients with tye1 diabetes and above 60 ercentages of atients with tye 2 diabetes have retinoathy. In the Wisconsin Eidemiologic Study of DR, 3 ercentages of younger-onset atients (tye 1 diabetes) and 1.6 ercentages of older-onset atients (tye 2 diabetes) were legally blind. In the younger-onset grou, 86 ercentages of blindness was attributable to DR. In the older-onset grou, in which other eye diseases were common, one-third of the cases of legal blindness were due to DR. One out of three diabetic erson resents sign of DR and one out of ten suffers from its most severe and visionthreatening forms. DR can be managed using available treatments, which are effective if diagnosed early. Since DR is asymtomatic until late in the disease rocess, regular eye fundus examination is necessary to monitor any changes in the retina. With the increasing revalence of diabetes and the aging oulation, it is exected that, in 2025, 333 millions diabetic atients worldwide will require retinal examination each year[2]. Fundus images with DR exhibit red lesions, such as Microaneurysms (MA) and Hemorrhages (HE), and bright lesions, such as exudates and cotton woolsots. Because of the variability in aearance of these lesions, different techniques have been designed to detect these lesions in DR detection systems. Diabetic retinoathy is a common comlication of diabetes. Diabetes mellitus often results in diabetic 411 Arathy.T

retinoathy which is caused by athological changes of the blood vessels which nourish the retina. DR occurs when the increased glucose level in the blood damages the caillaries, which nourish the retina. As a result of this damage, the caillaries leak blood and fluid on the retina. The visual effects of this leakage are features, such as Microaneurysms, hemorrhages, hard exudates, cotton wool sots or venous loos, of DR. AMD is a multi-factorial ocular disease caused by deterioration of cells in the macula. It is one of the leading causes of central vision loss in eole aged over 50 years. AMD is characterized by drusen, retinal igmentation, and atrohy of hotorecetors. It has several risk factors. They are, age, smoking, hyertension and family history.recent World Health Organization (WHO) reort reveals that 8million eole are affected with severe blindness due to AMD. Globally, United Nations estimates that 20 25 million eole are having AMD and this figure may increase to196millionin 2020 and 288 million in 2040[3]. AMD can be diagnosed by identifying drusen from the retinal fundus images. Automatic segmentation of drusen is needed to automate the diagnostic rocess. The texture of the retina background is directly analysed by means of LBP, and only this information is used to differentiate healthy atients and this athology. Glaucoma as the silent thief of sight which is a secific otic nerve disease with the rogressive break down of nerve fibres. It occurs due to the elevated ressure in the otic nerve head. The otic nerve fibres carry the image information to the brain. When a significant number of nerve fibres are damaged by high fluid ressure, blind sot develos in the field of vision and causes ermanent vision loss. It is the second leading cause of vision loss in the whole world and its rogression is exected to increase. Early diagnosis and otical treatment including a screening examination of the retinal fundus hotograhs can minimize vision loss. Glaucoma diagnosis is based on the atient s family medical history, thin corneas, high intraocular ressure and manual assessment of the ONH from the color fundus images. One of the glaucomatous changes observed in the color fundus images is the aearance of otic disc (OD)i.e., enlargement of the deression called cu and thinning of the neuroretinal rim.otic disc (OD) is the brightest feature in a normal fundus image and it has an ellitical shae. It aears bright orange-ink with a ale centre. Orange-ink aearance reresents the healthy neuro-retinal tissue. Due to athologies, the orangeink color gradually disaears and aears ale. Blood vessels and the otic nerves are emanating out from the OD. Its size is about one seventh of the entire image. The ale centre is devoid of neuro retinal tissue and is called the cu. The vertical size of this cu can be estimated in relation to the disc as a whole and resented as a cu-to-disc ratio. The cu-to-disc ratio (CDR) exresses the roortion of the disc occuied by the cu and it is widely acceted index for the assessment of glaucoma. For normal eye it is found to be 0.3 to 0.5 [4]. As the neuroretinal degeneration occurs the ratio increases and at the CDR value of 0.8 the vision is lost comletely. There is no cure of glaucoma yet, although it can be treated. Worldwide, it is the second leading cause of blindness [Global data on visual imairment in the year 2002]. It affects one in two hundred eole aged fifty years and younger, and one in ten over the age.of eighty years. The damage to the otic nerve from glaucoma cannot be reversed. However, lowering the ressure in the eye can revent further damage to the otic nerve and further eriheral vision loss. Figure 1deicts some examles of these diseases in comarison with the fundus image from a healthy atient [1],[4] (a) (b) (c) (d) Fig.1. Fundus images. (a) Healthy, (b) DR (with Microaneurysms and Exudates)(c) AMD (with drusen) and (d)glaucoma. The above exlanation states that, their early diagnosis allows, through aroriate treatment, to reduce costs generated when they are in advanced states and may become chronic. This fact justifies screening camaigns. However, a screening camaignrequires a heavy workload for trained exerts in the analysis of anomalous atterns of each disease which, added to the at-risk oulation increase, makes these camaigns economically infeasible. Therefore, the need for automatic screening systems is highlighted. Based on these facts, comuter-aided diagnosis software caable of 412 Arathy.T

discriminating, through image rocessing, between a healthy fundus (without any athology) and DR and AMD and Glaucoma atients was develoed. The final aim of the software roosed in this aer is to be used in an automatic screening and grading of these diseases making the at-risk oulation assessment ossible. The scoe of this roject is to focus in the Eye disease secifically Retina to develo an automatic system for comuter-aided screening and grading of DR,AMD,Glaucoma descritors. The images are resized using the length of the horizontal diameter of the fundus as reference. Bicubic interolation is used for resizing; the outut ixel value is a weighted average of ixels in the nearest 4-by-4 neighbourhood. Before feature extraction, a median filter for noise reduction is erformed using a 3-by-3 neighbourhood. In this re rocessing is used for better visualization. Image re-rocessing can II. PROPOSED METHOD An algorithm for retina image classification without the need for rior segmentation of susicious lesions was develoed[1]. The comlete block is follows in fig 2.Manual lesion segmentation is time consuming and automatic segmentation algorithms might not be accurate, thus removing the need for lesion segmentation can make the classification more robust. The algorithm is mainly based on the texture analysis of the retina background by means of LBP to detect whether the fundus is DR or AMD or normal. Also the total number of lesions is calculated to find the severity of these diseases.cdr is calculated to detect Glaucoma disease. A. Pre-rocessing Due to the fact that the images under study belong to different databases, the size of the images varies. As the LBP and VAR values deend on the radius of the neighbourhood, the images must be resized to a standardized size to obtain comarable texture Fig 2 Block Diagram of the roosed work significantly increase the reliability of an otical insection. Several filter oerations which intensify or reduce certain image details enable an easier or faster evaluation. In this work re rocessing is done with median filter for better visualization. Median Filter in images finds the median ixel value within the diameter that secified. It removes bright or dim features. Median filters are very effective in removing salt and eer and imulse noise while retaining image details because they do not deend on values which are significantly different from tyical values in the neighborhood. Median filters work in successive image windows in a fashion similar to linear filter. It sorts all the ixels in an increasing order and takes the middle one. If the number of ixels is even, the median is taken as the average of the middle two ixels after sorting. In median filtering, the neighboring ixels are 413 Arathy.T

rankedaccording to brightness (intensity) and the median value becomes the new value for the central ixel. In the median filtering oeration, the ixel values in the neighborhood window are ranked according to intensity, and the middle value (the median) becomes the outut value for the ixel under evaluation. To detect Glaucomawe convert the RGB image to CMY color model and extract magenta comonent after alying median filter. It is ossible to see that the cu is usually darker or brighter than other art of the image. That s why we choose magenta comonent. It will give the best erformance. B. Segmentation Only the ixels of the retina background are considered significant for the texture analysis. Thus the main structures of the fundus (the vascular network and the otic disc), which are not related to the diseases under study, should not be taken into account when the fundus texture is analysed. Some reliminary tests showed that if these redominant structures were included in the texture analysis, the differences between healthy and athological images were not areciated due to the similar asect of these structures. The otic disc and the vascular network are detected by multi thresholding. This segmentation technique is based on thresholding. Its basic rincile is to determine a value as a threshold, generally in a gray tone that is within the range of tones used in the image. For examle, in an image with an 8 bit resolution, the threshold may be between 0 and 255. After establishing the threshold of all the regions in the image, it is ossible to label every ixel, associating it to the value band established in each region. When there are just two regions for classification, one of these receives the label 0 and the other 1, and in this case the technique is called binarization. More than one threshold can be established in the same image; this technique is called multi-thresholding. This technique subdivides the image in more than two regions, establishing the lower and the higher limits of each region of interest. Since the otical disc in equalized image is corresonding to maximum brightness regions. Multi thresholding is based on Otsu s Thresholding Method. It is based on a very simle idea. We find the threshold that minimizes the weighted withinclass variance ormaximizing the between-class variance. It Oerates directly on the gray level histogram. To detect the severity of DR and AMD, we aly multithresholding for lesion segmentation after rercessing. Multi thresholding is used for lesion segmentation. After segmentation we calculate the total number of lesions in the fundus image. If the number if lesion is less than 100, severity is low. If the number of lesions is in between 100 and 5000,its severity is medium. If it is greater than 5000, severity is high To detect Glaucoma we use Multithresholding for the segmentation of otic disc and otic cu. After segmentation we calculate the vertical diameter of disc and cu. The vertical size of this cu can be estimated in relation to the disc as a whole and resented as a cu-to-disc ratio. The cu-to-disc ratio (CDR) exresses the roortion of the disc occuied by the cu and it is widely acceted index for the assessment of glaucoma VCD CDR [5]. For normal eye it is found to be 0.3 to 0.5. VDDCDR is defined as (1) VCD = Vertical Cu Diameter. VDD = Vertical Disc Diameter. The comuted CDR is used for glaucoma screening. When CDR is greater than a threshold, it is glaucomatous, else healthy eye. Usually for healthy eye the ratio is of 0.2-0.3 mostly.the CDR ratio is mostly calculated for glaucoma screening than other arameter. Also the roosed method reduces the time required for testing than other techniques. So that the atients can avail their result quickly. C. Featue extraction The LBP and VAR oerators described above are used to characterize the texture of the retina background. They are calculated for each ixel of the green images using P = 8 and different values of R (R = 1,2,3,5). The LBP and VAR values corresonding to ixel ositions of the otic disc, vessels or outside the fundus are not considered. The green comonents of image are indeendently analysed.the resulting LBP and VAR images rovide a descrition of the image texture. Different statistical information is extracted from these LBP and VAR histograms to use it as features in the classification stage. Concretely, the calculated statistical values are: mean, standard deviation, median, entroy, skewness and kurtosis. To sum u, 6 statistical values are calculated from each LBP and VAR histogram, giving lace to 12 features for each radius used. Consequently, the total number of 414 Arathy.T

features is equal to 48 (12 features x 4 radius x 1 comonents). Local binary atterns (LBP) are a owerful grey - scale texture oerator used in many comuter vision alications because of its comutation simlicity. The first ste in LBP is to roduce a label for each ixel in the image where the label is found based on the local neighbourhood of the ixel which is defined by a radius, R, and a number of oints, P. The neighbouring ixels are thresholded with resect to the grey value of the central ixel of the neighbourhood generating a binary string or, in other words, a binary attern. The value of a LBP label is obtained for every ixel by summing the binary string weighted with owers of two as follows: LBP P 1 P, R s( g g c ). 2 0 (2) Where g and g are the grey values of the c neighbourhood and central ixel, resectively. P reresents the number of samles on the symmetric circular neighbourhood of radius R. The g values c are interolated to fit with a given R and P. The values of the labels deend on the size of the neighbourhood (P). 2P different binary atterns can be generated in each neighbourhood. However, the bits of these atterns must be rotated to the minimum value to achieve a rotation invariant attern. In the case of P = 8, only 36 of the 2 ossible atterns are rotation invariant, i.e., LBP 8, R can have 36 different values. Figure 3 shows how LBP are calculated for a circular neighbourhood of radius 1 (R = 1) and 8 samles (P = 8). When LBP are used for texture descrition, it is common to include a contrast measure by defining the rotational invariant local variance as follows VAR P, R 1 P 1 P P 1 0 g P 1 0 ( g ) 2 (3) (4) Fig 3 LBP comutation: (a) Grey values of a circular neighborhood of radius 1 and 8 samles. (b) Thresholding between the grey value of the neighborhood and the central ixel. The rotation invariant local binary attern generated is 00101101 (the arrows indicate the order in which the string is formed). Usually using comlementary contrast leads to a better erformance than using LBP alone, but this is ignored in many comarative results.lbp only consider the signs of the difference to comute the final descritor. The information related to magnitude of the difference is comletely ignored. The magnitude rovides comlementary information that has been utilized to increase the discriminative ower of the oerator. Esecially in the neighbourhood with strong edges the magnitude of the difference can rovide imortant information. Magnitude of the difference is utilized to find the dominant direction in a neighbourhood. The dominant direction is defined as the index in the circular neighbourhood for which the difference is maximum. As an image undergoes rotation the dominant direction in a neighbourhood also undergoes the rotation by the same angle. Rotations of textural inut image cause the LBP atterns to translate into different location and to rotate about their origin.the LBP and VAR measures are comlementary and are combined to enhance the erformance of the LBP oerator. D. Classification Once the features are extracted, the data of the model set is classified using multiclass SVM. SVM are based on statistical learning theory and have the aim of determining the location of decision boundaries that roduce the otimal searation of classes.in the case of a two-class attern recognition roblem in which the classes are linearly searable the SVM selects from among the infinite number of linear decision boundaries the one that minimizes the generalization error. Thus, the selected decision 415 Arathy.T

boundary will be one that leaves the greatest margin between the two classes, where margin is defined as the sum of the distances to the hyerlane from the closest oints of the two classes.the data oints that are closest to the hyerlane are used to measure the margin, hence these data oints are termed suort vectors. Consequently, the number of suort vectors is small. If the two classes are not linearly searable, the SVM tries to find the hyerlane that maximizes the margin while, at the same time, minimizing a quantity roortional to the number of misclassification errors. The trade-off between margin and misclassification error is controlled by a user-defined constant. SVM were initially designed for binary (two -class) roblems. When dealing with multile classes, an aroriate multi-class method is needed. It comare one class with the others taken together. This strategy generates n classifiers, where n is the number of classes. The final outut is the class that corresonds to the SVM with the largest margin, as definedabove. For multi-class roblems one has to determine n hyerlanes.here we uses 3 hyerlanes. having to search different tyes of lesions. The only needed segmentation in the resented aroach is to mask the significant structures (vessels and otic disc) but their accuracy has little influence on the final result. For checking the severity, lesions are segmented. Severity is based on the total number of lesions. For assessment of glaucoma, cu-to-disc ratio is most widely acceted index. Here the cu and disc are determined by thresholding. For normal images CDR is found to be less than 0.5 where as for glaucoma images it is found to be greater than 0.8. The result of retinal disease screening is shown below. Figure 4 shows scaled, comonent searated,disk searated and vessel searated image of a normal fundus with a text box showing that it is a normal. Figure 5 shows the DR affected fundus,figure 6 shows AMD affected fundus and figure 7 shows Glaucoma affected fundus with severity level. III. RESULTS AND DISCUSSION Seven exeriments were conducted and validated with the roosed rocedure: AMD -Normal, DR - Normal, Glaucoma-Normal, and the severity of these diseases, and 4 class roblem (AMD - DR Glaucoma- Normal). This work makes use of the LBP oerator. The roosed method combines the features extracted from the green comonent. It is well known that the green comonent of the fundus image rovides a better visualization of the retinal structures comared to the other two color channels. There is no other system that analyses the texture of the retina background and detects AMD, DR and severity at the same time. Also the Glaucoma disease is detected based on CDR value. Many more AMD or DR detection techniques exist. But most of them focus on lesion segmentation instead of a study of the retina background. This fact makes the accuracy of the classification stage deendent on the accuracy of the lesion segmentation. Lesion segmentation involves a series of uncertainties and a non accurate segmentation may rovoke imortant errors in the classification. The main advantage of the rocedure roosed in this aer is that it gets a good erformance without Fig 4 Result of a normal fundus 416 Arathy.T

Fig 7 Result of a Glaucoma Affected Fundus Fig 5 Result of a DR affected fundus Fig 6 Result of a AMD Affected Fundus IV. CONCLUSION In this aer, a new aroach for AMD, DR and Glaucoma diagnosis was resented. It is based on analyzing texture discrimination caabilities in fundus images to differentiate healthy atients from AMD and DR images. The roosed method is caable of discriminating the classes based on analyzing the texture of the retina background, avoiding revious segmentation of retinal lesions. Such lesion segmentation algorithms might be both time consuming and otential inaccurate, thus avoiding the segmentation is beneficial. The obtained results demonstrate that using LBP as texture descritor for fundus images rovides useful features for retinal disease screening.for the detection of glaucoma, firstly, otic disk need to be segmented. After image acquisition, rerocessing is done by alying median filter. The otic disk and cu is segmented using various multithresholding. Then CDR is calculated and classification is done for deciding whether condition of eye is normal or glaucomatous.. In this aer CDR is determined for both glaucoma affected and normal fundus images. The method Success for all images due to the resence of re rocessing technique..the CDR value gives the rogression about the disease. 417 Arathy.T

REFERENCES [1] 1.Sandra Morales, Kjersti Engan, Valery Naranjo and Adran Colomer (2015) Retinal Disease Screening Through Local Binary Patterns,Biomedical and Health Informatics [2] 2.L. Tang,M. Niemeijer, and M. Abramo (2011)Slat Feature Classication:Detection of the Presence of Large Retinal Hemorrhages,8th Symosium (International) on Biomedical Imaging. (ISBI), 681-684. [3] 3.M. R. K. Mookiah, U. R. Acharya, H. Fujita, J. E. Koh, J. H. Tan,K. Noronha, S. V. Bhandary, C. K. Chua, C. M. Lim, A. Laude, and L. Tong (2015 )Local Configuration Pattern Features for Age-Related Macular Degeneration Characterization and Classification,Comuters in Biology and Medicine, 63, 208 218 [4] 4.M. Lakshmi and M. Baby (2014) Glaucoma Detection from Color Fundus Images Using Multithresholding Method with Median Filter, International Journal of Innovative Research & Develoment,3,123-127 418 Arathy.T