This article deals with a histological problem by using image processing and feature extraction in images of renal tissues of rats and their classification through various  methods such as: Bayesian inference, decision trees and support vector machines

Cabrita, AS

Capela e Silva, F

Nunes, B

Rafael, A

Rato, Luis

Repositório Científico da Universidade de Évora

Processing and Classification of Biological Images: Application toHistologyBruno Nunes & Luı´s Miguel RatoDepartamento de Informa´ticaCITI - Centro de Investigac¸a˜o em Tecnologias de Informac¸a˜o da Universidade de E´voraUniversidade de E´vora, E´vora, PortugalFernando Capela e SilvaDepartamento de BiologiaInstituto de Cieˆncias Agra´rias e Ambientais Mediterraˆnicas (ICAAM)Universidade de E´vora, E´vora, PortugalAna Rafael & Anto´nio Silve´rio CabritaInstituto de Patologia Experimental, Faculdade de Medicina da Universidade de CoimbraUniversidade de Coimbra, Coimbra PortugalABSTRACT: This article deals with a histological problem by using image processing and feature extractionin images of renal tissues of rats and their classification through various methods such as: Bayesian inference,decision trees and support vector machines.Keywords: Image Processing, Segmentation, Classi-fication, Kidney, Renal Glomeruli, Histology, FeatureExtraction, Xenobiotics1 INTRODUCTIONThe observation in photonic microscopy of histo-logical sections allows us to evaluate the structuralchanges of an individual’s exposure to xenobiotics.This evaluation can be made through quantitativecriteria by using computational tools and algorithmsto make the process faster and more accurate.2 OBJECTIVESThis work intends to evaluate the morphologicalchanges in the kidney of rats, caused by exposure toxenobiotics, through computer-assisted histomorpho-metric analysis.3 IMAGE PROCESSINGIn this work, we used 210 kidney photomicrographsof 21 Wistar rats belonging to three groups: Group I[Control, n=7], Group II [Pesticide Thiram, n=6) andGroup III [Corn Oil, n=8] (Fialho et al. 2001).Samples were collected after 35 days of testing andprocessed by histological routine techniques, in Insti-tuto de Patologia Experimental, Faculdade de Medic-ina da Universidade de Coimbra.The final preparations were observed with a NikonEclipse 600 microscope, using a magnification of200X, and the images were acquired using a NikonDN100 digital camera.For each animal of each group was selected a sec-tion and, for each of them, there were randomly ob-served 10 renal corpuscles (Figure 1).Figure 1: Example of a renal corpuscleThe images were then processed using several toolsfrom the program ImageJ (Rasband 2009) togetherwith an existing plugin called MultiCell Outliner(MCO) (Latxiondo 2006), in order to highlight the re-nal corpuscles.3.1 METHODOLOGYThe methodology we used was divided in two differ-ent steps: the delimitation and the extraction of therenal corpuscle.3.1.1 DELIMITATIONIn this first step, the objective was to define thecontour of the renal corpuscle and fill it’s interior.Depending on the three complexity degrees of theimages: low, medium and high, there were differenttypes of approach.Low Complexity ImagesWe say that an image has a low complexity de-gree when the Bowman’s capsule is clearly visibleand without any breaks, as we can see in Figure 2.Figure 2: Low complexity renal corpuscleIn these cases, we were able to fully trace the out-line of the renal structure using only the MCO plugin,as left image from Figure 3 shows.Once the outline is highlighted, we’ve painted it’sinterior with the black color, using the Fill tool, asshown in the right image from Figure 3.Figure 3: Delimitation and filling of a low complexity renal cor-puscleThe result from a low complexity image delimita-tion is shown in Figure 4.Figure 4: Final result from a delimitation in a low complexityimageMedium Complexity ImagesIn images with a medium complexity degree, de-spite it contains some breaks, the Bowman’s capsuleshape is perfectly recognizable.Figure 5: Medium complexity renal corpuscleIn these cases, we only managed to outline the renalcorpuscle partially using MCO plugin. The followingfigure shows the partial delimitation step by step.Figure 6: Partial delimitation of a medium complexity renal cor-puscleAfter the partial delimitation was completed, theinside of each highlighted part was painted with theblack color using the Fill tool.Figure 7: Filling the partial delimitation of a medium complexityrenal corpuscleThe following figure shows the result after the par-tial delimitation of the renal corpuscle.Figure 8: Partial delimitation in a medium complexity caseTo complete the process, it was necessary to manu-ally finish the Bowman’s capsule’s contour, using thebrush tool with 3 pixels size.Figure 9: Final result from the complete delimitation of amedium complexity renal corpuscleHigh Complexity ImagesImages with high complexity degree are the worstcase scenario. In these images, the Bowman’s cap-sule’s shape is difficult to identify or barely can beseen.Figure 10: High complexity renal corpuscleWe didn’t obtained satisfactory results using theMCO plugin in these cases, so the renal corpuscle hadto be entirely delimited by hand, using the brush toolwith 3 pixels size. The delimitation steps and the finalresult can be seen in figure below.Figure 11: Delimitation of a high complexity renal corpuscle3.1.2 EXTRACTIONIn the second step, the objective was to isolate the re-gions painted in black previously, under the form ofbinary images.To do this, we’ve made a RGB to Grayscale con-version, as can be seen in Figure 12.Figure 12: RGB to Grayscale conversionAfter this conversion, each of the image’s pixelsstops storing an RGB color value and starts to store agray intensity, being the strongest intensity the blackcolor and the weakest intensity the white color.Then we’ve applied the threshold technique on topof the grayscale image, in order to highlight the renalcorpuscle silhouette, as shown in Figure 13.Figure 13: Highlight of renal structure through threshold tech-nique4 FEATURE EXTRACTIONAfter isolating the renal corpuscles, and based on theresulting images from processing and segmentation,we used ImageJ again to measure several morpho-logical features that served to identify and distinguishthe renal corpuscles.The following features were considered:Figure 14: Diameter and Perimeter of Bowman’s capsuleFigure 15: Bowman’s capsule’s and glomeruli’s total areaFigure 16: Exterior and interior Bowman’s spaceFigure 17: Total Bowman’s space and glomerular capillaries areaIn addition to these features, we’ve also consideredthe fractal dimensions of the renal corpuscle.Finally, after measuring all these features for thecomplete set of images, the resulting data was storedin the form of a vector of features for each image.5 CLASSIFICATIONOnce the entire data set was obtained, we applied sev-eral algorithms for classifying data using WEKA (Wit-ten and Frank 2005). The used classifiers were:• Zero Rule• One Rule• Naive Bayes• J48 Tree• Support Vector MachinesThe classification results, with and without feature se-lection, may be seen in Table 1.Table 1: Percentage of accuracy rate of the different classifiersClassifier No Selection SelectionZero Rule 71,3636 71,3636One Rule 70,4545 72,7273NaiveBayes 64,5455 73,6364J48 Tree 70,4545 73,1818SVM 69,0909 78,1818In order to reduce uncertainty in the final resultswe made some transformations on the data, namelythe aggregation of image data concerning to the sameanimal.The classification results after aggregation of in-stances, with and without feature selection, may beseen in Table 2.Table 2: Percentage of accuracy rate of the different classifierswith instance aggregationClassifier No Selection SelectionZero Rule 71,4286 71,4286One Rule 66,6667 66,6667NaiveBayes 85,7143 85,7143J48 Tree 71,4286 66,6667SVM 76,1905 80,9524Comparing the results there have been some im-provements on some classifiers, however, a high ac-curacy rate not always means a good classification.This can be observed through the example of Tables 3and 4, corresponding to Naive Bayes and Zero Ruleconfusion matrices, both with feature selection andinstance aggregation.Table 3: NaiveBayes confusion matrixClassified as→ Healthy PesticideHealthy 12 3Pesticide 0 6Naive Bayes has 85,7143% of accuracy rate andcorrectly classifies all the pesticide instances and themost of the healthy instances.Table 4: Zero Rule confusion matrixClassified as→ Healthy PesticideHealthy 15 0Pesticide 6 0On the other hand, Zero Rule classifies well all thehealthy instances but fails completely when classi-fying the pesticide instances. On this case, the highaccuracy rate (71,4286%) is purely justified with theexistence of more healthy instances then pesticide in-stances.One way to avoid these type of misunderstandings,it’s to consider other variables such as false positiverate and precision of the class of interest. The tablebelow shows values from both variables for each clas-sifier.Table 5: FP Rate and Precisison of the different classifiers withinstance aggregation for pesticide classClassifier FP Rate PrecisionZero Rule 0 0One Rule 0,267 0,429NaiveBayes 0,200 0,667J48 Tree 0,333 0,500SVM 0,200 0,571Given these facts, when choosing one of the classi-fiers we should always consider not only it’s accuracyrate but also the the lowest false positive rate an thehighest precision on the class of interest. Therefore,based on the previous tables, we can conclude that thebest classifier options would be NaiveBayes followedby SVM.6 CONCLUSIONThe results of this work may be considered positivesince, by analyzing the information obtained throughall the process, it was possible to say with an accept-able degree of certainty, whether or not histologicalchanges occured at the level of renal corpuscles. How-ever, given that there remains an uncertainty in the re-sults of some classifiers, improvements can be madeby including other image features.REFERENCESFialho, I., F. Capela e Silva, A. Rafael, & A. Cabrita (2001).Effects of tetramethylthiuram disulfide (thiram) on kidneymetallothionein expression in rats. In Biomarkers of Envi-ronmental Contamination, 24-26 September 2001, Po´voa deVarzim, Portugal, pp. 191.Latxiondo, K. (2006). Multicell outliner.http://rsbweb.nih.gov/ij/plugins/multi-cell-outliner.html.[accessed July-2010].Rasband, W. S. (1997-2009). ImageJ. Bethesda, Mary-land, USA: U. S. National Institutes of Health.http://rsb.info.nih.gov/ij/ [accessed July-2010].Witten, I. H. & E. Frank (2005). Data Mining: Practical Ma-chine Learning Tools and Techniques (2nd ed.). MorganKaufmann.

Processing and classification of biological images: Application to histology

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Processing and classification of biological images: Application to histology

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