A Multiresolution Analysis Framework For Breast Tumor Classification Based On DCE-MRI

Transcription

1 A Muliresoluion Analysis Framewor For Breas Tumor Classificaion Based On DCE-MRI Alexia G. Tzalavra¹, Evangelia I. Zacharai², Niolaos N.Tsiaparas¹, Foios Consaninidis³ and Konsanina S.Niia¹ ¹School of Elecrical and Compuer Engineering Naional Technical Universiy of ²Deparmen of Compuer Engineering and Informaics Universiy of Paras ³NHS Greaer Glasgow & Clyde Glasgow, UK Ahens Rio, Greece Ahens, Greece Absrac Ιn his paper, a muliresoluion approach is proposed for exure characerizaion of breas umors in dynamic conras-enhanced magneic resonance images. The decomposiion scheme represened by he saionary wavele ransform (SWT) is invesigaed in erms of is abiliy o discriminae beween malignan and benign umors. The mean and enropy of he deail subimages produced for he specific decomposiion scheme are used as exure feaures. The exraced feaures are subsequenly provided ino a linear classifier in a leave-one-ou cross-validaion seing. The experimenal resuls for he proposed feaures exhibi high performance, when compared o he exising approaches, wih he classificaion accuracy approaching Keywords breas umor diagnosis, DCE-MRI, exure, wavele ransforms I. INTRODUCTION Breas cancer is he mos common cancer among women and he second leading cause of cancer deahs in women oday [1]. More specifically, in 2012 nearly 1.7 million new cases were diagnosed in women worldwide [2] while cases were repored in Europe [3]. Furhermore, according o he American Cancer Sociey abou 1 in 8 (12%) women in he US will develop invasive breas cancer during heir lifeime [4]. The early deecion of breas cancer can be he ey o increased survival raes and also o more specific and less aggressive herapy opions. Highly specific breas cancer deecion mehods eliminae he ris of unnecessary biopsies or surgical procedures. Breas magneic resonance (MR) imaging has emerged as a promising modaliy for breas cancer deecion [5]. Dynamic conras-enhanced MR imaging (DCE-MRI) involves assessing he changes in signal inensiy over ime. This follows he inravenous injecion of a paramagneic conras agen [6]. Several machine learning approaches have been proposed o analyze breas DCE-MRI daa. The specific mehods vary no only regarding he feaures exraced bu also he classificaion echniques used. A wide range of feaures have used in breas umor Compuer Aided Diagnosis (CAD) sysems. Dynamic feaures [7, 8] have been used o characerize he emporal enhancemen paern of a umor, while archiecural feaures [7, 8] have been exraced o characerize he morphology of he umor. Moreover, ineic [9, 10] and exure feaures [11, 12] have been used o disinguish beween malignan and benign umors. More specifically, Yao e al. [12] compued exural feaures based on he co-occurrence marix and also exraced frequency feaures by applying he discree wavele ransform (DWT) on he exure emporal sequences of he breas umors in order o classify hem. Shannon e al. [13] applied exural ineics, o capure spaioemporal changes in breas lesion exure in order o disinguish malignan from benign lesions. Furhermore, spaioemporal feaures have been proved o exhibi high performance in characering breas umors. Zheng e al. [14] used a spaioemporal enhancemen paern involving Fourier ransformaion and Gabor filers o analyze breas umors. Gal e al. [15] exraced spaioemporal feaures from a parameric model of conras enhancemen. Furhermore, several classificaion mehods have been used in breas umor CAD sysems. More specifically, Twellman e al. [16] presened a classificaion echnique using arificial neural newors. Zheng e al. [14] assessed he diagnosic performance of he feaures hey exraced for differeniaing beween benign and malignan umors using linear discriminan analysis (LDA). Yao e al. [12] used suppor vecor machines (SVM) for breas umor classificaion. The DWT has been widely used in several exure classificaion mehods in medical images [17, 18] due o is muliresoluion characerisics. However, he DWT suffers from lac of shif invariance. The Saionary Wavele Transform (SWT) overcomes he specific lac of he DWT, since i is shif-invarian. The SWT has no been used in breas DCE-MRI analysis. In his paper we propose a breas umor classificaion echnique using he SWT on he emporal enhancemen of DCE-MRI daa. To his end, a number of basic funcions derived from differen wavele families, are benchmared in erms of heir abiliy o discriminae beween malignan and benign umors. Fisher Linear Discriminan Analysis (LDA) was used as classifier /14/$ IEEE

2 II. METHODS The proposed sudy invesigaes he performance of muliresoluion exure feaures for discriminaing beween benign and malignan breas umors using wo dimensional (2D) DCE-MRI. The mehodology consiss of he following main seps: umor segmenaion, normalizaion across subjecs, feaure exracion from he umor region and umor classificaion ino malignan or benign. In his sudy, umor segmenaion was manually performed by an exper breas radiologis. Preprocessing involves he same seps as in [11]. More specifically, he breas umors are firs spaially normalized in order o eliminae scale variaions. Fourier ransform is subsequenly applied o capure he emporal enhancemen properies. Then SWT is performed in emporal enhancemens o characerize he spaial variaions of he umor. Texure feaures are exraced from he SWT deail subimages. Tumor classificaion is performed using Fisher LDA on he exraced feaures. The pipeline is illusraed in Fig.1. More deails are provided nex. Tumor Segmenaion Tumor Normalizaion Feaures Exracion Fig.1 Ouline of he proposed mehod DFT Transformaion SWT Transformaion LDA classificaion A. Tumor normalizaion The 2D umor regions are normalized by performing eigendecomposiion of he covariance marix associaed wih he disribuion of pixels in a given umor region. Tumor regions are hen roaed and scaled o ensure ha heir principal direcions are aligned wih a reference coordinae space and heir mos significan eigenmodes become idenical o a predefined size. B. Temporal Enhancemen Modelling In DCE-MRI images, he emporal enhancemen for a pixel p is defined as follows: ( ) (, ) (,0) I( p,0) I p I p C p, =, = 1... T 1 (1) where I(p, ) denoes he inensiy of p a a scanning ime and T is he oal number of ime insances. The daa used consised of T=4 insances, hence 3 emporal enhancemen maps were obained for each umor. The invesigaion of he emporal response of various issue ypes is of major imporance in umor diagnosis. The Fourier ransform has been considered as ey in capuring pixelwise emporal enhancemen properies in image analysis. A pixelwise 1D discree Fourier Transform (DFT) has been performed in he enhancemen curve C (p,) of each pixel p. Thus, T-1 DFT coefficiens are obained for each pixel. Each DFT coefficien represens a disincive emporal enhancemen map, which illusraes he frequency conen of he corresponding emporal enhancemens [7]. C. Saionary Wavele Transform Images usually conain informaion a muliple resoluions. Therefore muliresoluion analysis has emerged a useful framewor for many image analysis ass in which DWT played a major role [17, 18]. However, a drawbac of he DWT is ha i is no shif invarian. The SWT is a wavele ransform algorihm designed o overcome he lac of shif invariance of he DWT. More specifically, he DWT of a signal x[n] is defined as is inner produc wih a family of funcions, j ψ and j, which form an orhonormal se of vecors, a combinaion of which can compleely define he signal. The funcions j () he prooype scaling ψ and j consis of versions of () and wavele ψ funcions, discreized a level j and a ranslaion. However, for he implemenaion of he DWT, only he coefficiens of a lowpass and a high-pass half-band filer are required, which saisfy he following condiions: () h[ ] j, ψ j () g[ ] + = j 1,0, (2) + = ψ 1,0 j, I shall be noed ha SWT is similar o he DWT, bu no downsampling is performed. Insead, upsampling of he lowpass and high-pass filers is carried ou. For images, i.e., 2-D signals, he 2-D SWT can be used. This consiss of a SWT on he rows of he image and a SWT on he columns of he resuling image. The decomposiion of he image yields four subimages (one approximaion and hree deail images) for every level of decomposiion. Figure 2 illusraes a schemaic diagram of he 2- D SWT. Fig.2 Schemaic diagram of he 2-D SWT. For j = 0, A0 is he original image.hr, Hc, Gr, and Gc are he low-pass and high-pass filers on he rows and columns of each subimage. In our sudy, he SWT ransform was performed in each of he emporal enhancemen maps for each umor region. D. Texure feaures exracion The maximum value of decomposiion equals o min(log2n, log2m), where N is he number of rows and M is

3 he number of columns of he image. Therefore, for our experimens where N=M=150, he maximum level of decomposiion equals o 7. The deail subimages conain he exural informaion in horizonal, verical, and diagonal orienaions. The approximaion subimages were no used for exure analysis because hey are he rough esimae of he original image. The exure feaures ha were esimaed from each deail subimage were he mean and enropy of he absolue value of he deail subimages, boh commonly used as exure descripors. (a) III. CLASSIFICATION A. Linear Discriminan Analysis LDA wih Fisher linear discriminan rule was used o perform he classificaion of he umor regions. The specific classifier has been incorporaed wihin a leave-one-ou cross validaion scheme where a classifier is rained wih all bu one sample. The lef ou sample is reaed as a es sample o be classified accordingly. The process is repeaed unil all samples are seleced as he lef ou sample. Classificaion rae is finally compued as he mean of correcly classified samples. IV. EXPERIMENTAL RESULTS A. Tesing Daa The images used in his sudy were provided by Universiy of Pennsylvania. They were acquired from paiens wih breas umors in a 1.5 T scanner (Siemens Sonaa) or a 3 T scanner (Siemens Trio) [11]. In oal, here were 44 subjecs used in our experimens, including 23 malignan and 21 benign cases. All of hese samples were hisologically verified. The boundary of he suspicious umors was oulined on he images by an exper breas radiologis. Examples of benign and malignan umors are shown in Fig. 3. Fig.3 Examples of SWT images for 3 levels of decomposion for (a) a malignan and (b) a benign umor. The images in he firs row correspond o he iniial images.for he images in rows 2-4, each column corresponds o he deail subimages of he levels 1-3 respecively. B. Resuls Table I shows he classificaion resuls produced by he LDA classifier on he exraced feaures for 3 levels of decomposiion. The highes accuracy scores in he LDA case for each basic funcion and 3 levels of decomposiion was 91%. The specific score was obained by using he SWT afer he emporal enhancemen of he iniial images wih he sym9 moher wavele, for hree levels of decomposiion (as indicaed by bold ype in Τable Ι). Furhermore he area under he curve (AUC) in he aforemenioned decomposiion scheme is I shall be noed ha he 3-level decomposiion scheme resuled in 9 deail subimages for each ime insance; hence oally 27 deail subimages and consequenly 54 exure feaures were obained. (b) TABLE I CLASSIFICATION RESULTS FOR DIFFERENT WAVELET FAMILIES AND 3 LEVELS OF DECOMPOSITION Muliresoluion SWT Scheme Classificaion Resuls (%) LDA ACC SN SP haar coif coif db db db db sym sym sym sym bior

4 bior bior bior bior ACC:accuracy, SN:sensiiviy, SP:specificiy, bior:biorhogonal, db:daubecies, sym:symle, coif:coifle REFERENCES V. CONCLUSION In his wor, we invesigaed he possibiliy of using he SWT ransform o characerize he exure of breas umors in DCE-MRI afer firs having capured heir emporal enhancemen by performing DFT. To his end, differen SWT decomposiion levels and basis funcions were invesigaed. Texure feaures were exraced from he SWT images and fed ino an LDA classifier. The experimenal resuls illusraed high accuracy raes in breas umor classificaion. Previous sudies on classificaion of breas DCE-MRI umors yielded various performances. Zheng e al. [14] repored ha he area under he curve (AUC) approaches 0.97 when using spaioemporal feaures and an LDA classifier. In he specific sudy he feaures are exraced from he boundaries of he segmened umors. However, a drawbac of he specific mehod is ha accurae umor boundary deecion should is needed prior o he classificaion. Gal e al. [15] repored an AUC of 0.91 ± 0.06 when using 2 of heir proposed spaioemporal feaures. Yao e al. [12] when compued exural feaures and also exraced frequency feaures by applying DWT on he exure emporal sequences of he breas umors repored an AUC of and in he raining and es daa respecively. Shannon e al. [13] repored a classificaion accuracy of 89% when ineic aribues were combined wih morphologic descripors. Because he daases, image analysis mehods and classifiers used in hese sudies are differen, a direc comparison canno be aemped. However i could be argued ha he proposed mehodology can be considered efficien for breas umor classificaion. More specifically, he specific mehodology achieves high classificaion raes wih low compuaional cos. Therefore i can be concluded ha he use of he SWT afer he DFT in breas DCE-MRI umors can be ey o umor classificaion. The specific sudy demonsraed ha wavele-based exure analysis may be promising in breas umor diagnosis. Addiional sudies, sysemaically applying he proposed mehodology o larger populaions and several addiional classifiers are expeced o verify our findings. We also plan o incorporae auomaed segmenaion ino he curren worflow. ACKNOWLEDGEMENTS The auhors wish o han Dr. Sarah Englander and Dr. Michell Schnall from Universiy of Pennsylvania, USA, who suppored he collecion of he daa. This research has been cofinanced by he European Union (European Social Fund ESF) and Gree naional funds hrough he Operaional Program "Educaion and Lifelong Learning" of he Naional Sraegic Reference Framewor (NSRF) - Research Funding Program: Thales. Invesing in nowledge sociey hrough he European Social Fund. [1] Imaginis, Breas Cancer: Sympoms & Treamen Resources, 2014, hp:// [2] hp:// er_saisics.php [3] Ferlay, Seliarova-Foucher, Lore-Tieulen, Rosso, Coebergh,Comber, Forman,Bray, Cancer incidence and moraliy paerns in Europe:Esimaes for 40 counries in 2012, European Journal of Cancer, vol. 49, pp , 2013 [4] American Cancer Sociey, Wha are he ey saisics abou breas cancer?, 2014 [5] hp:// [6] Susan G. Orel, Michell D. Schnall, MR imaging of he breas for he deecion, diagnosis, and saging of breas cancer, Radiology 220, 2001,13-30 [7] M. D. Schnall e al., Diagnosic archiecural and dynamic feaures a breas MR imaging: Mulicener sudy, Radiology vol. 238, pp , 2006 [8] K. G. A. Gilhuijs e al., Compuerized analysis of breas lesions in hree dimensions using dynamic magneic-resonance imaging, Medical Physics, vol.25, pp , 1998 [9] Weijien Chen, Maryellen L. Giger, Ulrich Bic and Gillian M. Newsead, Auomaic idenificaion and classificaion of characerisic ineic curves of breas lesions on DCE-MRI, Med. Phys., vol. 33, pp , 2006 [10] Lee SH, Kim JH, Kim KG, e al. Opimal clusering of ineic paerns on malignan breas lesions: comparison beween K-means clusering and hree-ime-poins mehod in dynamic conras-enhanced MRI, Engineering in Medicine and Biology Sociey, 2007 [11] P. Gibbs and L. W. Turnbull, Texural analysis of conras-enhanced MR images of he breas, Magn. Reson. Med., vol. 50, pp , 2003 [12] Jianhua Yao, Jeremy Chen and Caherine Chow, Breas Tumor Analysis in Dynamic Conras Enhanced MRI Using Texure Feaures and Wavele Transform, IEEE Journal of seleced opics in signal processing, vol. 3, No 1, 2009 [13] Shannon C. Agner, Salil Soman, Edward Libfeld, Margie McDonald, Kahleen Thomas, Sarah Englander, Mar A. Rosen, Deanna Chin, John Nosher and Anan Madabhushi, Texural Kineics: A Novel Dynamic Conras-Enhanced (DCE)-MRI Feaure for Breas Lesion Classificaion, Journal of Digial Imaging, vol. 24, issue 3, pp , 2010 [14] Yuanjie Zheng and Sarah Englander, Sajjad Baloch, Evangelia I. Zacharai, Yong Fan, Michell D.Schnall, Dinggang Shen, STEP: Spaioemporal enhancemen paern for MR-based breas umor diagnosis, Medical Physics, vol 36, No 7, July 2009 [15] Yaniv Gal, Andrew Mehner, Andrew Bradley, Dominic Kennedy and Suar Crozier, New Spaioemporal Feaures for Improved Discriminaion of Benign and Malignan Lesions in Dynamic Conras- Enhanced Magneic Resonance Imaging of he Breas, Journal of Compu er Assised Tomography, vol.35, No 5, 2011 [16] T. Twellmann, O. Liche and T. W. Naemper, An adapive issue characerizaion newor for model-free visualizaion of dynamic conras-enhanced magneic resonance image daa, IEEE Trans. Med. Imag., vol. 24, no. 10, pp , 2005 [17] M. Mojsilovic, M. V. Popovic, A. N. Nesovic, and A. D. Popovic, Wavele image exension for analysis and classificaion of infraced myocardial issue, IEEE Trans. Biomed. Eng., vol. 44, no. 9, pp , 1997 [18] D.-R. Chen, R.-F. Chang, W.-J. Kuo, M.-C. Chen, and Y.-L. Huang, Diagnosis of breas umors wih sonographic exure analysis using

5 wavele ransform and neural newors, Ulrasound Med. Biol., vol. 28, no. 10, pp , 2002