USING DSIFT AND LCP FEATURES FOR DETECTING BREAST LESIONS

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1 USING DSIF AND LCP FEAURES FOR DEECING BREAS LESIONS Semih ERGİN 1, Onur KILINÇ 2 1 Department of Electrical and Electronics Engineering, Osmangazi University, Eskisehir, URKEY 2 Department of Electrical and Electronics Engineering, Anadolu University, Eskisehir, URKEY {sergin@ogu.edu.tr, onur_kilinc@anadolu.edu.tr} ABSRAC his research promises a two class classification of digitized mammogram images using two feature extraction algorithms; Dense Scale Invariant Feature ransform (DSIF) and Linear Configuration Pattern (LCP). he dataset that is used for this research is retrieved from Image Retrieval in Medical Applications (IRMA) project which provides mammographic patches consisting of normal, benign and malignant cases of publicly available digital mammographic databases. In this research, 200 normal and 200 abnormal lesion cases of Digital Database for Screening Mammography (DDSM) are used. Both linear configuration pattern (LCP) and DSIF feature vectors have a high accuracy in classifying normal cases versus abnormal up to 100%. his approach can be used in computer-aided diagnosis to help radiologists to distinguish a healthy case from others in an efficient way. KEYWORDS Breast Lesion, Computer-Aided Diagnosis, Dense Scale Invariant Feature ransform, Linear Configuration Pattern, Digital Mammography, Decision ree, Fisher Linear Discriminant Analysis. 1 INRODUCION Breast cancer is a commonly seen fatal disease on the world. he estimated annual occurrence of this disease is more than a million and resulting more than 400,000 of death according to the world health organization (Meselhy Eltoukhy et al., 2010). It is vital to diagnose breast cancer on early phases to reduce the number of deaths (Rangayyan et al., 2007). However, detection of breast cancer regions is troublesome so that even human eye of a specialist misses 10% to 30% on mammographic screening (Jaffar et al., 2011). Even though, not all identified lesions on mammograms turn into fatal phases of breast cancer, false negative results leaves patients with an uncertain future. Accordingly, avoidance of such cases is important and developing an efficient computer-aided diagnosis (CAD) is needed to help radiologists for better diagnosis of breast cancer on X-ray mammograms. Feature extraction is a crucial step in computeraided diagnosis in medical imaging. It must be done by discarding irrelevant features and keeping representative ones. By doing so, relative features for the desired cases are extracted not only good representation but also affordable sized feature vector quantization which results better classification and computational speed. It can be done by either space transformation or spatial feature extraction directly. A lot of work had already been done to represent tumor areas conveniently. Discrete Wavelet ransform (DW), Overcomplete Wavelet ransform (OW) with Haar wavelets are used to understand if there is a mass presence on the image or not with an accuracy of almost 90% (Angelini et al., 2006). On another approach, instead of using feature dimension, DW of images are used directly with a cascaded Support Vector Machine (SVM) and an accuracy of nearly 80% is retrieved (Campanini et al., 2004). Spatial features like contrast, homogeneity, inverse difference moment, entropy and energy is used with throughout co-occurrence matrices and 93 % accuracy obtained (De Oliveira Martins et al., 2009). he purpose of this work is to set up a framework that can distinguish two classes; normal tissue versus abnormal. In order to do so, two rotation invariant descriptors are used to extract features from the acquired database. Feature extraction step is clearly told in Section 2 while in Section 3 classifiers are mentioned. Section 4 presents the database that is used for this approach and performance evaluation of the classifiers. Finally in Section 5 conclusion part is presented. 2 FEAURE EXRACION Feature extraction is a challenging step of classifying abnormal tissues, benign and malignant structures. It is essential to create representative features for each region of interest (ROI) patch. However, there is only little difference between a healthy case and an early step of a lesion. here are many approaches of feature extraction; e.g., Gabor features (Buciu and Gacsadi,

2 2011), wavelet decomposition (Ferreira and Borges, 2003), Principal Component Analysis (Bellotti et al., 2006). Local Binary Pattern (LBP) method (Ojala et al., 1996), method is also an effective method for abnormal tissue detection in computed tomography (C). For the related work, local configuration pattern technique and dense scale invariant feature transform (DSIF) descriptors are used. 2.1 Linear Configuration Pattern (LCP) Features Local configuration pattern is a method to describe an area with local information and circularly shifted histogram of pattern occurrences which make rotationally invariant. LCP features can be used to describe both the microscopic features represented by optimal model parameters and local features represented by pattern occurrences (Guo et al., 2011). Pattern occurrences, refers to local structure of an image and calculation is based on local LBP which labels the pixels of a grey level image by using circular neighbourhood of each pixel. LBP method uses a circular neighbourhood for a given radius R to each pixel which can be seen in Eq. (1). P1 (1) i LBP P, R u( gi gc)2, i0 P is the number of pixel samples. is the intensity of pixel number whilst is the intensity of center pixel. ough LBP methods are superior different illumination differences, if local structure is a smooth area, the center pixel values are not too far from nearby pixels; resulting nearly the same final vector as all pixel values are far away from center pixel. o get rid of this problem, local variance information (VAR) is to be calculated for creation of the histogram. LBP riu 2 P, R P1 ugi ( gc), ifulbppr, 2 i 0 (2) P 1, otherwise P1 1 2 VAR ( gi µ ) (3) P i0 In Eq. (2), u( x ) is the unit-step function and U is the uniformity measure to define uniform patterns, decided by the number of spatial transitions. In Eq. (3), μ refers to the average intensity of nearby pixels. Consequently, this method combines with microscopic configuration modelling that represents the textural property of the image and different pattern occurrences as shown in Eq. (2). 2.2 Dense Scale Invariant Feature ransform Using of DSIF descriptor is a known method of feature extraction of lesions (Song et al., 2012). DSIF performs scale invariant feature transform on nonoverlapping image blocks with the given radius and returns a 128-dimensional feature vector for each are spanned with the radius. 3 CLASSIFIERS For the proposed work, two different classification techniques, namely Fisher Linear Discriminant Analysis (FLDA) and Decision ree are used to make a two-class categorization. 3.1 Fisher Linear Discriminant Analysis Fisher Linear Discriminant Analysis (FLDA) (Gülmezoglu et al., 2005) is a considerably important method in pattern recognition applications. Fisher s classification method differs in solving multi-class problems compared with others by considering both feature class similarities and betweenclass similarities. o do so, Fisher used an LDA that maximizes the rate of between-class similarity and within-class similarity. Fisher s maximization criterion can be defined as in Eq. (4). JW ( ) = r W S W W S W (4) 1 ( W ) ( B ) Fisher s S W matrix refers to within-class variation while S B matrix refers to between-class variation. W is the projection matrix. In this optimization criterion, the subspace that is to be retrieved is one less then class number. Working in this subspace results bad recognition rates. According to previous studies, this optimization criterion is derived as follows: ww wsw i t j 0, i j is to be used instead of =0, i j i j where i,j= 1, 2,...,d and S= S + S. he between-class scattering matrix can t w B be written in terms of differences of averages of classes. hen, between-class matrix is weighted by eigenvalues of within-class matrix.

3 3.2 Decision ree A well-known and widely used classifier Decision ree (Ye et al., 2000) algorithm has two options. First one is the target class or variable and the other one is the predictor value. raining phase continues a recursive operation on training data samples labelled by target values until a stopping factor is met. For each partition, example sets have relatively small target value. he branch is to be stopped when all samples be of the same class or targeted value which generates a decision tree leaf. are benign and 100 are malignant cases. Figure 1 shows 3 samples of each class on the used part of the DDSM database. 4 EXPERIMENAL SUDY In this study, a mammographic database utilized by IRMA project is categorized into two different classes; normal and abnormal. Using the LCP and DSIF descriptors, rotation invariant and scale invariant transform operations are met are and in order to reduce dimension to affordable sizes, feature transform is applied to the vectors. hese vectors are used to train widely known classifiers; FLDA and Decision ree. Performance evaluation part is done by using 10-fold cross validation. 4.1 Database It is crucial to work on data with ground truth when dealing with medical image applications. An exhaustive work of categorizing some of the publicly available mammographic databases reference to Breast Imaging Reporting Data System (BI-RADS) has already been made (Deserno et al., 2011). Utilized set of data includes four popular medical image databases of 12 classes (Oliveira et al., 2008). 12 classes are divided into 4 main tissue density class including 3 lesion class each. able 1 shows how many patches included for each database. able 1: he number of available mammographic patches in the IRMA project DAABASE OAL IMAGES IRMA 69 MIAS 150 DDSM 2796 LLNL 1 In the proposed framework, 400 almost entirely fat tissue of Digital Database for Screening Mammography (DDSM) images are chosen randomly to create normal and abnormal classes. Among them 200 are normal, 100 Figure 1: Patches of 128 x 128 pixels from the DDSM database. First row represents 3 normal cases, second row represents 3 benign lesions, whilst the third row shows 3 malignant lesions. 4.2 Extraction of Feature Vectors In computer vision applications, it is important to create features independent of rotation and scale to make detections more accurate. he illustrated work includes two rotation invariant feature extraction techniques; DSIF and LCP. For the chosen part of retrieved database from the IRMA project, 1 pixel radius and neighbouring pixel number of 8 parameters are used to create LCP feature vectors. his method returns 1 x 81 feature vectors independent of the input image size. his vector size clearly indicates a dimension reduction when it is compared with the raw mammogram image size (128 x 128). he elements of the vectors are circularly shifted to its largest value at the last position. Largest values, which represents the pattern occurrences, are discarded for each vector to reduce domination over configuration coefficients. Last form of LCP feature vectors are 1 x 80 dimension and are used as fed to train classifiers. Using the DSIF feature extraction technique of VL feat (Vedaldi and Fulkerson, 2010), each patch is divided into 10 x 10 sized non-overlapping blocks and features are extracted referenced to fast DSIF variant with radius of 5 which returns feature vectors of 128 x 400 for each patch (Felzenszwalb et al., 2008). Since one-dimensional feature vectors are needed for classifiers, dimension reduction is applied as follows.

4 After reshaping each column into rows, time-domain features are computed. Energy, mean, standard deviation, max, kurtosis and skewness are evaluated and concatenated which results 6 x 128 feature vectors. Finally all feature vectors are turned into 768 x 1 size and used to train classifiers. 4.3 Classification Results For related framework, two classification algorithms are used to understand abnormality presence of chosen part of DDSM database. For reliable accuracy results, ten-fold cross validation is used to determine classification accuracy. able 2 shows the classification performance for the first order SVM classifier after 10- fold cross validation. able 2: Fisher linear discriminant analysis classification results. FLDA Classification Accuracy % esting Cross Val Interval LCP DSIF No Fold Fold Fold Fold Fold Fold Fold Fold Fold Fold Average 99,30 99,80 able 3 shows the classification performance for Decision ree classifier using after 10-fold cross validation. able 3: Decision ree classification results. Decision ree esting Classification Accuracy % Cross Val Interval No LCP DSIF Fold Fold Fold Fold Fold Fold Fold Fold Fold Fold Average 99,30 100,00 5 CONCLUSION Detection of lesions on digitized mammograms is a cumbersome work in computer vision applications. Since there is only little difference between a lesion and a normal tissue, even radiologists have success rate in correct diagnosis of 75% (Buciu and Gacsadi, 2011). Reporting values show that both DSIF and LCP features combined with two classification algorithms FLDA and Decision ree leads to a classification rate of maximum of 100%. he presented framework outperforms the previous works in the literature in terms of accuracy. Although, some results derived from the tests are inferior to others, it is not necessary to reach a high accuracy of classification in each classifier to build up a computer aided diagnosis application. For a future work, improved classification can be done for all tissue types on digital mammograms by adding pre-processing and feature selection techniques. ACKNOWLEDGEMENS he database utilized in this study was used by the courtesy of.m. Deserno, Dept. of Medical Informatics, RWH Aachen, Germany. REFERENCES Angelini, E., Campanini, R., Iampieri, E., Lanconelli, N., Masotti, M., Roffilli, M., esting the performances of different image representations for mass classification in digital mammograms. International Journal of Modern Physics C. 17, Bellotti, R., De Carlo, F., angaro, S., Gargano, G., Maggipinto, G., Castellano, M., Massafra, R., Cascio, D., Fauci, F., Magro, R., Raso, G., Lauria, A., Forni, G., Bagnasco, S., Cerello, P., Zanon, E., Cheran, S.C., Lopez orres, E., Bottigli, U., Masala, G.L., Oliva, P., Retico, A., Fantacci, M.E., Cataldo, R., De Mitri, I., De Nunzio, G., A completely automated CAD system for mass detection in a large mammographic database. Medical Physics. 33, Buciu, I., Gacsadi, A., Directional features for automatic tumor classification of

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