Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being sufficiently accurate for real-life implementations.



In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification. of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space

Cetin, Mujdat

Ekin, Ahmet

Ercil, Aytul

Erçil, Aytül

Soldea, Octavian

Unay, Devrim

Çetin, Müjdat

Ünay, Devrim

English

Sabanci University Research Database

Automatic Annotation of X-ray Images: A Studyon Attribute SelectionDevrim Unay1, Octavian Soldea1, Ahmet Ekin2, Mujdat Cetin1, and AytulErcil11 Computer Vision and Pattern Analysis Laboratory, Faculty of Engineering andNatural Sciences, Sabanci University, Turkey, unay@sabanciuniv.edu,2 Video Processing and Analysis Group, Philips Research, The Netherlands.Abstract. Advances in the medical imaging technology has lead to anexponential growth in the number of digital images that need to beacquired, analyzed, classified, stored and retrieved in medical centers.As a result, medical image classification and retrieval has recently gainedhigh interest in the scientific community. Despite several attempts, theproposed solutions are still far from being sufficiently accurate for real-life implementations.In a previous work, performance of different feature types were inves-tigated in a SVM-based learning framework for classification of X-Rayimages into classes corresponding to body parts and local binary pat-terns were observed to outperform others. In this paper, we extend thatwork by exploring the effect of attribute selection on the classificationperformance. Our experiments show that principal component analysisbased attribute selection manifests prediction values that are compara-ble to the baseline (all-features case) with considerably smaller subsetsof original features, inducing lower processing times and reduced storagespace.1 IntroductionStoring, archiving and sharing patient information among medical centers hasbecome a crucial task for the medical field. Companies as well as governments arenow in anticipation of building Patient Centric IT systems such as that targetedby the Ratu e-health project of Northern Finland3 that focus on building a largenational electronic patient records archive.Digital medical images, such as standard radiographs (X-Ray) and computedtomography (CT) images, represent a huge part of the data that need to be storedin medical centers. Manual labeling of this data is not only time consuming, butalso error-prone due to inter/intra-observer variations. In order to realize anaccurate classification one needs to develop tools that allow high performanceautomatic image annotation, i.e. labeling of a given image with some text orcode without any user interaction.3 http://pre20090115.stm.fi/pr1105954774022/passthru.pdfSeveral attempts in the field of medical images have been performed in thepast. For example, the WebMRIS system [1] aims at retrieving cervical spinalX-Ray images, whereas the ASSERT system [2] focuses on retrieving CT imagesof the lungs. While these efforts consider retrieving a specific body part only,other initiatives have been taken in order to retrieve multiple body parts.The ImageCLEF Medical Image Annotation task, run as part of the Cross-Language Evaluation Forum (CLEF) campaign, is a yearly held medical im-age annotation challenge for automatic classification of an X-Ray image archivecontaining more than 10,000 images randomly taken from the medical routine.The ImageCLEF Medical Annotation dataset contains images of different bodyparts of people from different ages, of different genders, under varying viewingangles and with or without pathologies. Depending on the year of the challenge,participants are asked to automatically annotate these images according to clas-sification labels that vary from 58 to 196.A potent classification system requires the image data to be translated into amore compact and more manageable representation containing only the relevantfeatures. Several feature representations have been investigated in the past forsuch a classification task. Among others, image features, such as average valueover the complete image or its sub-regions [3] and color histograms [4], havebeen investigated. Recently in [5], texture features such as local binary patterns(LBP) [6] have been shown to outperform other types of low-level image featuresin classification of X-Ray images. One drawback of the mentioned work is thelarge number of features extracted, which may be problematic for the classifi-cation step. Retaining only the relevant features by applying attribute selectionon local binary patterns, may lead to comparable classification accuracies withsmaller feature sets.Motivated by the considerations above, in this paper we explore the effect ofprincipal component analysis based feature selection on the performance of localbinary patterns applied to the ImageCLEF-2009 Medical Annotation dataset.The paper is organized as follows. Section 2 presents our feature extraction,feature selection and classification steps in detail. Section 3, introduces the imagedatabase and the experimental evaluation process performed. Next, in Section 4,corresponding results are presented. Finally, Section 5 outlines our conclusion.2 MethodIn this work we utilize the image data from the ImageCLEF-2009 Medical Anno-tation task for training and testing. 12677 fully classified and unbalanced X-Rayimages are available to train a classification system, which needs to be evaluatedusing 2000 unlabeled images according to four different label sets including 58to 196 distinct classes. Please note that, the data is unbalanced meaning someclasses have significantly larger share among data than others.2.1 Feature ExtractionWe extract spatially enhanced local binary patterns as features from each imagein the database. LBP [6] is a gray-scale invariant local texture descriptor with lowcomputational complexity. The LBP operator labels image pixels by thresholdinga neighborhood of each pixel with the center value and considering the resultsas a binary number. The neighborhood is formed by a symmetric neighbor set ofP pixels on a circle of radius R. Formally, given a pixel at (xc,yc), the resultingLBP code can be expressed in the decimal form as follows :LBPP,R(xc, yc) =P−1∑n=0s(in − ic)2n (1)where n runs over the P neighbors of the central pixel, ic and in are the gray-level values of the central pixel and the neighbor pixel, and s(x) is 1 if x ≥ 0 and0 otherwise.After labeling an image with a LBP operator, a histogram of the labeledimage fl(x, y) can be defined asHi =∑x,yI(fl(x, y) = i), i = 0, . . . , L− 1 (2)where L is the number of different labels produced by the LBP operator, andI(A) is 1 if A is true and 0 otherwise.The derived LBP histogram contains information about the distribution oflocal micro-patterns, such as edges, spots and flat areas, over the image. Follow-ing [6], not all LBP codes are informative, therefore we use the uniform versionof LBP and reduce the number of informative codes from 256 to 59 (58 informa-tive bins + one bin for noisy patterns). Following [7], we divide the images into4x4 non-overlapping sub-regions and concatenate the LBP histograms extractedfrom each region into a single, spatially enhanced feature histogram (Figure 1).This step aims at obtaining a more local description of the image.Finally, we obtain a total of 944 features per image, which is a large numberfor the classification step. Therefore, we apply principal component analysisbased feature selection.2.2 Feature Selection: Principal Component AnalysisPrincipal component analysis (PCA, or Karhunen-Loev´e transform) [8] is a vec-tor space transformation often used to reduce multidimensional datasets to lowerdimensions for analysis.Given data X consisting of N samples, in PCA we first perform data normal-ization by subtracting the mean vector m from the data. Then the covariancematrix Σ of the normalized data (X −m) is computed.m =1NN∑i=1Xi (3)Σ = (X −m)(X −m)T (4)Fig. 1. The image is divided into 4x4 non-overlapping sub-regions from which LBPhistograms are extracted and concatenated into a single, spatially enhanced histogram.Afterwards, the basis functions are obtained by solving the algebraic eigenvalueproblemΛ = ΦTΣΦ (5)where Φ is the eigenvector matrix of Σ, and Λ is the corresponding diagonalmatrix of eigenvalues. Feature selection is then performed by keeping q (q < N)orthonormal eigenvectors corresponding to the first q largest eigenvalues of thecovariance matrix. Here, q is empirically set such that total variance measuredfrom these eigenvalues correspond to a user-defined percentage.2.3 Classification: Support Vector MachinesSVM [9] is a popular machine learning algorithm that provide good results forgeneral classification tasks in the computer vision and medical domains: e.g.nine of the ten best models in ImageCLEFmed 2006 competition were based onSVM [10]. In a nutshell, SVM maps data to a higher-dimensional space usingkernel functions and performs linear discrimination in that space by simultane-ously minimizing the classification error and maximizing the geometric marginbetween the classes.Among all available kernel functions for data mapping in SVM, Gaussianradial basis function (RBF) is the most popular choice, and therefore it is usedhere.RBF : K(xi,xj) = exp(−γ ‖ xi − xj ‖2), γ > 0 (6)where γ is a parameter defined by the user. Besides γ, there exists an errorcost C that controls the trade-off between allowing training errors and forcingrigid margins. An optimum C value creates a soft margin while permitting somemisclassifications. In this work we used LibSVM library (version 2.89) [11] forSVM and empirically found its optimum parameters (γ and C) on the dataset.Fig. 2. Distribution of the data labeled in 2005 (left) and 2008.3 Experimental Setup3.1 Image DataThe training database released for the ImageCLEF-2009 Medical Annotationtask includes 12677 fully classified (2D) radiographs that are categorized into 57classes in 2005 and 196 classes in 2008. Their distribution with respect to theseclasses is displayed in Figure 2.3.2 EvaluationIn order to avoid domination of attributes with greater numeric ranges over smallones, we linearly scale each feature to [-1,+1] range before presenting them tothe SVM.We evaluate our SVM-based learning using 5-fold cross validation, wherethe database is partitioned into five subsets. Each subset is used once for testingwhile the rest are used for training, and the final result is assigned as the averageof the five validations. Note that for each validation all classes were equallydivided among the folds. We measure the overall classification performance usingaccuracy, which is the number of correct predictions divided by the total numberof images.4 ResultsWe compare our classification results of SVM with PCA-based feature selection(referred to as SVMrbf+PCA in the Figures 3-4) with two reference approaches:1)baseline (No PCA) that refers to the SVM classification with all availablefeatures (PCA is not applied), and 2)random guess meaning the classifier putsall the data to the class with the highest frequency.Fig. 3. Effect of PCA on classification accuracy with LBP features and 2005 labelsused (57 classes).Figure 3 shows the effect of PCA-based feature selection on the classifica-tion accuracy of SVM for the data with 2005 labels (57 classes). Notice thatwhen all the LBP feature set is input to the SVM (baseline case), the overallaccuracy is measured as 88%, while random guess is at the level of 28%. On theother hand, with attribute selection we reach accuracy levels (87,5%) compara-ble to the baseline case with only about 150-200 features out of possible 944.This leads to considerable reduction in prediction time as well as storage space.This observation shows that although the used LBPu2 operator inherently dis-cards non-informative patterns from the feature set, we can further refine theseattributes using PCA without degrading classification accuracy.Figure 4 shows the effect of PCA-based feature selection on the classificationaccuracy of SVM for the data with 2008 labels (196 classes). For this case,baseline accuracy is measured as 83,4%, while random guess is at the level of18%. Similar to the previous observations, performing feature selection with PCAresults in accuracy values (83%) comparable to the baseline with approximately150-200 features.In terms of computational expense (on a PC with 2.13GHz processor and2GB RAM), the baseline approach exhibits 25.5min processing time with 151MBstorage space required for the cross-validation task. On the other hand, for theproposed PCA-based approach these values are measured as 4.4min and 23MB,respectively. In consequence, the proposed PCA-based approach provides an over5-fold improvement in processing time and storage space requirements.Fig. 4. Effect of PCA on classification accuracy with LBP features and 2008 labelsused (196 classes).5 ConclusionIn this paper we have introduced a classification work with the aim of auto-matically annotating X-Ray images. We have explored the effect of PCA-basedfeature selection on the efficacy of recently popular and highly discriminativelocal binary patterns within a SVM-based learning framework. Our experimentson the ImageCLEF-2009 Medical Annotation database revealed that applyingattribute selection on local binary patterns provide comparable classificationaccuracies with considerably smaller number of features, leading to reduced pro-cessing time and storage space requirements.References1. Long, L.R., Pillemer, S.R., Lawrence, R.C., Goh, G.H., Neve, L., Thoma, G.R.:WebMIRS: web-based medical information retrieval system. In Sethi, I.K., Jain,R.C., eds.: Society of Photo-Optical Instrumentation Engineers (SPIE) ConferenceSeries. Volume 3312. (December 1997) 392–4032. Shyu, C.R., Brodley, C.E., Kak, A.C., Kosaka, A., Aisen, A.M., Broderick, L.S.:Assert: a physician-in-the-loop content-based retrieval system for hrct imagedatabases. Comput. Vis. Image Underst. 75(1-2) (1999) 111–1323. Rahman, M.M., Desai, B.C., Bhattacharya, P.: Medical image retrieval with proba-bilistic multi-class support vector machine classifiers and adaptive similarity fusion.Computerized Medical Imaging and Graphics 32(2) (2008) 95 – 1084. Mueen, A., Sapian Baba, M., Zainuddin, R.: Multilevel feature extraction andx-ray image classification. J. Applied Sciences 7(8) (2007) 1224–12295. Jacquet, V., Jeanne, V., Unay, D.: Automatic detection of body parts in x-rayimages. In: Mathematical Methods in Biomedical Image Analysis, 2009. MMBIA2009. IEEE Computer Society Workshop on. (2009)6. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotationinvariant texture classification with local binary patterns. Pattern Analysis andMachine Intelligence, IEEE Transactions on 24(7) (2002) 971–9877. Ahonen, T., Hadid, A., Pietika˜inen, M.: Face recognition with local binary pat-terns. In: Lecture Notes in Computer Science : Computer Vision - ECCV 2004.(2004) 469–4818. Jolliffe, I.T.: Principal Component Analysis. Second edn. Springer (October 2002)9. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition.Data Mining and Knowledge Discovery 2(2) (1998) 121–16710. Mu¨ller, H., Deselaers, T., Deserno, T., Clough, P., Kim, E., Hersh, W.: Overviewof the imageclefmed 2006 medical retrieval and medical annotation tasks. In:Evaluation of Multilingual and Multi-modal Information Retrieval. (2007) 595–60811. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. (2001)Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Automatic annotation of X-ray images: a study on attribute selection

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