Glaucoma is an irreversible pathology, generated by increased intraocular pressure. Early detection is critical and can pre- vent total vision loss. Clinical examinations are commonly used to detect the disease. Still, the time and cost of identi- fication is quite high. This paper presents a computational methodology that aims to assist specialists in the discov- ery of glaucoma through Computer Vision techniques. The proposed methodology consists in the application of several texture descriptors combined with a parameter optimiza- tion done through Grid search with the XGBoost classifier. A result was obtained with accuracy of 82.37% and ROC of 82.08%

Abreu, Pablo Vieira de

Araújo, Rafael Luz

Carvalho, Nonato Rodrigues de Sales

Filho, Antônio Oseas de Carvalho

Júnior, Francisco das Chagas A. C.

Lima, Thiago José Barbosa

Rodrigues, Maria da Conceição Leal Carvalho

Sousa, Deusimar Damião de

English

Universidade Salvador: Portal de Periódicos UNIFACS

Automatic Method for Glaucoma Classification UsingTexture Analysis, Xgboost and Grid searchNonato R. de S. CarvalhoUniversidade Federal do Piauínrdesales@ufpi.edu.brAntônio Oseas deCarvalho FilhoUniversidade Federal do Piauíantoniooseas@ufpi.edu.brFrancisco das Chagasdos A. C. JúniorUniversidade Federal do Piauífranciscojn49@gmail.comThiago José BarbosaLimaUniversidade Federal do Piauíthiagojb12@ufpi.edu.brMaria da Conceição L. C.RodriguesInstituto Federal do Piauímariacarvalho@ifpi.edu.brPablo Vieira de AbreuUniversidade Federal do Piauípablolukan@hotmail.comRafael Luz AraújoUniversidade Federal do Piauírafaluzaraujo@ufpi.edu.brDeusimar Damião deSousaUniversidade Federal do Piauídeusimar@ufpi.edu.brABSTRACTGlaucoma is an irreversible pathology, generated by increasedintraocular pressure. Early detection is critical and can pre-vent total vision loss. Clinical examinations are commonlyused to detect the disease. Still, the time and cost of identi-fication is quite high. This paper presents a computationalmethodology that aims to assist specialists in the discov-ery of glaucoma through Computer Vision techniques. Theproposed methodology consists in the application of severaltexture descriptors combined with a parameter optimiza-tion done through Grid search with the XGBoost classifier.A result was obtained with accuracy of 82.37% and ROC of82.08%.KeywordsGlaucoma; Grid search; XGBoost1. INTRODUCTIONGlaucoma can be defined as an optic neuropathy, whichcan compromise the entire optic nerve structure, cause dam-age to the nervous system and also increase intraocular pres-sure [16]. The World Health Organization (WHO) reportsthat glaucoma is the second leading cause of irreversibleblindness in the world [22]. Studies show that by 2020 morethan 79 million people could be carriers of glaucoma world-wide [2].Glaucoma is an asymptomatic disease in its early stages,which leads many patients to be unaware of the disease untilit reaches advanced stages, which is why an early diagnosisPermission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. To copy otherwise, or republish, to post on servers or to redistributeto lists, requires prior specific permission and/or a fee.that enables timely treatment is important [7]. Statistically,the increase of intraocular pressure does not indicate thepresence of glaucoma, but normal intraocular pressure doesnot exclude the presence of the disease. This implies thatthe diagnosis of glaucoma should be based primarily on theanalysis of the optic disc through fundus images [17].Diagnoses of diseases in the field of ophthalmology requireanalysis of large quantities of images. The image analysisprocess for the diagnosis of glaucoma is still done manuallyin a slow and subjective manner. In addition, results mayvary among professionals with a very high workload [24].In this context, this work presents an automatic methodthat applies the descriptors Gray-Level Co-Occurrence Ma-trix (GLCM) and Local Binary Pattern (LBP), combinedwith Grid search parameter estimator and Extreme GradientBoosting (Xgboost) classifier, with to provide an improve-ment in the classification of healthy glaucomatous images.2. RELATEDWORKSKnown studies on the problem of classification of fundusimages have become relevant over the years and have becomepopular in the scientific world. Table 1 shows a summary ofthe main information from related works for the classifica-tion of glaucomatous images.3. METHODOLOGYIn this section we expose the methodology used to de-velop the proposed method. Figure 1 provides a summaryof the steps taken: Image Acquisition, Feature Extraction,Classification, and Validation Metrics.3.1 Image AcquisitionThe base used for this work was the RIM-ONE R2 [23],composed of 455 retinal images. The previous diagnosticprocess was performed by specialists, who determined a setof 200 glaucoma images and 255 healthy images. In order tousing the proposed method, the Optical Disc regions weresegmented from masks made available by specialists fromthe base itself.Revista de Sistemas e Computação, Salvador, v. 10, n. 1, p. 84-88, jan./abr. 2020 http://www.revistas.unifacs.br/index.php/rscFigure 1: Methodology.Figure 2: Examples of RIM-ONE R2 base images.(a) Normal, (b) Glaucoma.3.2 PreprocessingAt this stage, logarithmic intensity transformation wasused for image enhancement, increasing the contrast of in-tensity ranges or even binarizing the images. This transfor-mation maps a narrow range of low input intensity valuesto a wider range of output levels, which can be expressed inEquation 1 [11] where, in the proposed method, C = 80S = C ∗ log(1 + r) (1)in which C is a constant and r is the pixel value.Figure 3: Examples of RIM-ONE R2 base images.(a) Without preprocessing, (b) Image (a) With pre-processing using logarithmic intensity transforma-tion.3.3 Feature ExtractionIn the proposed method, we used descriptors that extractinformation related to: spatial distribution, luminosity vari-ation, smoothness, roughness, regularity, surface structuresand the relationships between nearby pixels [21].GLCM is a texture descriptor that analyzes competitionsbetween pixel pairs, storing their relative intensities in asquare matrix. Co-occurrence probabilities are calculatedbetween two gray levels i and j, using the angles (0o, 45, 90oe 135o) and a distance named as pixel pair spacing [12].LBP is used for feature extraction in the image recogni-tion and classification process. In this method the intensityof each pixel of an image is replaced by a binary vector,determined by comparing each neighboring pixel with thecentral pixel, where the size of the neighborhood is 3x3,constituting the default limit value. The values obtained byneighbors are concatenated and the binary number gener-ated is converted to the decimal base to replace the centralvalue [20].3.4 CLASSIFICATIONAfter the image characterization process, the extractedfeatures were classified. This process is done with and with-out parameter optimization through Grid search and theExtreme Gradient Boosting classifier, using the cross vali-dation method to give more consistency to the results.Grid search.It implements a fit and score method. It also implements“predict”, “predict proba”, “decision function”, “transform”and “inverse transform” if they are implemented in the esti-mator used. The estimator parameters used to apply thesemethods are optimized by cross-validated grid search on aparameter grid [14].XGBoost.The XGBoost or Extreme Gradient Boosting algorithm isbased on the gradient boosting machine (GBM) technique[10] and can be applied in the context of supervised learning.The method uses predictors (trees) that minimize the chosenloss function. The loss function consists of two factors: acalculated error rate on validation and a smoothing factor.XGBoost is scalable and its details are described in [4].Cross validation.It is a computationally intensive technique that uses allavailable examples of training and test samples. This type ofunderperformance estimation lacks computational efficiencybecause the training process is repetitive, but the ultimategoal is to decrease the estimate variance [25].3.5 Validation MetricsTo evaluate the performance of the proposed method willbe used the evaluation metrics: sensitivity, specificity, accu-racy [3], Kappa [6] and Receiver Operating Charac teristic(ROC) [19].For evaluation of the test four situations are possible:1. True Positive (TP): An unhealthy image is correctlyclassified;2. False Positive (FP): A healthy image is considered ill;3. True Negative (TN): A healthy image is consideredclassified;4. False Negative (FN): An unhealthy image is consideredhealthy.Sensitivity (S) indicates the percentage of times the test85Revista de Sistemas e Computação, Salvador, v. 10, n. 1, p. 84-88, jan./abr. 2020 http://www.revistas.unifacs.br/index.php/rscTable 1: RELATED WORKS.Working Used Techniques Base Sample Accuracy[15] Deep learning Various Chinese Clinics 48.116 92.9%[5]Use of texture descriptorsAnd Convolutional Neural (CNN)DRISHTI e RIM-ONEin its 3 versions873 91,06%[1] Diversity Indexes RIM-ONE version 2 455 93,41%[9]Image Decompositionand Bayesian OptimizationRIM-ONE version 2 455 91,24%identify sick images, as defined by Equation ( 2).S =TPTP + FN. (2)Specificity (P) is the percentage of times that the testidentify healthy images, being defined by Equation ( 3).P =TNTN + TP. (3)Accuracy (A) is the percentage of cases classified correctly,regardless of what is positive and what is negative, beingdefined by Equation ( 4).A =TP + TNTP + FP + TN + FN. (4)The Kappa index (K) can be classified according to Table2, suggested by [13]. The closer the Kappa value is to 1, thegreater the agreemen.Table 2: Levels of classification accuracy accordingto the Kappa index.I´ndice Kappa (k) QualityK < 0.2 Poor0.2 ≤ K < 0.4 Reasonable0.4 ≤ K < 0.6 Good0.6 ≤ K < 0.8 Very goodK ≥ 0.8 ExcellentSource: [13]The receiver operating curve, also called Receiver Oper-ating Characteristic (ROC), is the area of TPR versus FPRvarying the threshold. These metrics are summarized inequations 5 and 6 [18].TPR = S =TPTP + TN(5)FPR = (1− S) = TPFP + TN(6),in which, TPR represents true positive rate and FPR falsepositive rate.The ROC chart provides an elegant way to present vari-ous confusion matrices produced at different boundaries. AROC traces the relationship between the true positive rateand the false positive rate, as shown in Figure 4 [18].4. RESULTSAfter the acquisition of the images, indicated in Section3.1, and the preprocessing exposed in Section 3.2, tests wereperformed to evaluate the proposed method. The tests wereFigure 4: ROC chart illustrationSource: [18]performed on a set of 455 images, 200 glaucomatous and 255healthy. Tables 3 and 4 present the results.Based on the data presented in Table 3, using the GLCMdescriptor the best result obtained was in the red channelwith the parameters of: distance = 1 and angle = 90. With-out applying the Grid search, it was obtained an accuracyof 75,77% and ROC of 75,73%. With the Grid search, it wasobtained an accuracy of 76,87% and ROC of 76,68%.Based on the data presented in Table 4, using the LBPdescriptor the best result was the green channel with theparameters: radius = 5 and number of neighbors = 10.Without applying the Grid search, an accuracy of 81,71%and ROC of 81,43%. With the Grid search, an accuracy of82,37% and ROC of 82,08%.The results obtained with the proposed method indicatethat Grid search makes it possible to achieve better resultsin the green and red channels.Cross-validation brings greater assurance to these results.Each channel of the Optical Disc (OD) image is capable ofproducing a different representation of the anatomy of thefundus of the eye. The Optical Disc and its edges are mostvisible in the red channel. This property is used to checkfor changes caused by glaucoma that are close to the edgesof the OD. The green channel has better contrast and, as inthe blue channel, the excavation is more visible because itspixels have a higher intensity (brighter) than the pixels inthe rest of the OD [8].5. CONCLUSIONThis paper presents a computational methodology thataims to assist specialists in the quantification of glaucomathrough Computational Vision techniques. The proposedmethodology uses LBP and GLCM to characterize the tex-86Revista de Sistemas e Computação, Salvador, v. 10, n. 1, p. 84-88, jan./abr. 2020 http://www.revistas.unifacs.br/index.php/rscTable 3: Classification results applying the GLCM descriptor and Logarithmic Intensity Transformations.Without Grid Search With Grid SearchChannel A (%) S (%) E (%) ROC (%) K (%) A (%) S (%) E (%) ROC (%) K (%)Green 70,27 67,53 73,00 70,27 40,28 71,80 68,18 74,60 71,39 42,7469,85 66,00 73,70 69,85 39,77 70,04 65,84 73,41 69,62 39,2969,37 66,15 72,58 69,37 38,61 68,94 65,44 71,48 68,46 36,69Red 75,55 73,29 77,18 75,24 50,16 76,87 75,67 77,69 76,68 52,7075,77 75,56 75,89 75,73 50,21 73,78 71,20 75,66 73,34 46,5675,77 75,56 75,89 75,73 50,21 73,78 71,20 75,66 73,24 46,56Blue 70,04 66,84 72,34 69,59 38,90 71,14 69,71 72,04 70,87 40,6768,28 65,21 70,37 67,79 35,10 70,48 67,18 72,90 70,04 39,8668,28 65,21 70,37 67,79 35,10 68,72 65,42 71,05 68,23 36,13Table 4: Classification results applying the LBP descriptor and Logarithmic Intensity Transformations.Without Grid Search With Grid SearchChannel A (%) S (%) E (%) ROC (%) K (%) A (%) S (%) E (%) ROC (%) K (%)Green 81,71 77,61 85,24 81,43 63,09 82,37 78,74 85,42 82,08 64,3780,61 77,33 83,26 80,30 60,72 82,37 79,60 84,58 82,09 64,2580,61 80,00 81,04 80,52 60,32 80,39 76,69 83,46 80,08 60,34Red 76,43 72,54 79,60 76,07 52,26 75,55 72,68 77,69 75,18 50,2076,43 72,54 79,60 76,07 52,26 75,11 71,68 78,17 74,73 49,5375,77 71,49 79,35 75,42 51,00 74,88 70,33 78,77 74,55 49,28Blue 77,75 72,68 82,35 77,51 55,23 77,97 72,81 82,70 77,75 55,7075,99 72,50 78,74 75,62 51,26 77,75 76,06 78,94 77,50 54,5475,55 70,37 80,25 75,31 50,80 77,75 75,78 79,16 77,47 54,59ture and parameter optimization using the grid search in theXgboost classifier. The tests were performed on the RIM-ONE R2 database and yielded promising results in which thegrid search showed a considerable improvement in the per-formance of the glaucoma classification. For a future work,we intend to evaluate the impact that grid search has on theperformance of the results of other classifiers and descrip-tors.6. REFERENCES[1] J. D. L. Araujo et al. Diagno´stico de glaucoma apartir de imagens de fundo de olho utilizando ı´ndicesde diversidade. 2018.[2] J. Ayub, J. Ahmad, J. Muhammad, L. Aziz, S. Ayub,U. Akram, and I. Basit. Glaucoma detection throughoptic disc and cup segmentation using k-meanclustering. In 2016 International Conference onComputing, Electronic and Electrical Engineering(ICE Cube), pages 143–147. IEEE, 2016.[3] M. Bland. An introduction to medical statistics.Oxford University Press (UK), 2015.[4] T. Chen and C. Guestrin. Xgboost: A scalable treeboosting system. 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AUTOMATIC METHOD FOR GLAUCOMA CLASSIFICATION USING TEXTURE ANALYSIS, XGBOOST AND GRID SEARCH

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AUTOMATIC METHOD FOR GLAUCOMA CLASSIFICATION USING TEXTURE ANALYSIS, XGBOOST AND GRID SEARCH

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