Tissue Classification using Cluster Features for Lesion Detection in Digital Cervigrams

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1 Tssue Classfcaton usng Cluster Features for Leson Detecton n Dgtal Cervgrams Xaole Huang 1, We Wang 1, Zhyun Xue 2, Sameer Antan 2, L. Rodney Long 2, Jose Jeronmo 3 1 Department of Computer Scence and Engneerng, Lehgh Unversty, PA, USA 2 Communcatons Engneerng Branch, Natonal Lbrary of Medcne, MD, USA 3 Dvson of Cancer Epdemology and Genetcs, Natonal Cancer Insttute, MD, USA ABSTRACT In ths paper, we propose a new method for automated detecton and segmentaton of dfferent tssue types n dgtzed uterne cervx mages usng mean-shft clusterng and support vector machnes (SVM) classfcaton on cluster features. We specfcally target the segmentaton of precancerous lesons n a NCI/NLM archve of 60,000 cervgrams. Due to large varatons n mage appearance n the archve, color and texture features of a tssue type n one mage often overlap wth that of a dfferent tssue type n another mage. Ths makes relable tssue segmentaton n a large number of mages a very challengng problem. In ths paper, we propose the use of powerful machne learnng technques such as Support Vector Machnes (SVM) to learn, from a database wth ground truth annotatons, crtcal vsual sgns that correlate wth mportant tssue types and to use the learned classfer for tssue segmentaton n unseen mages. In our experments, SVM performs better than un-supervsed methods such as Gaussan Mxture clusterng, but t does not scale very well to large tranng sets and does not always guarantee mproved performance gven more tranng data. To address ths problem, we combne SVM and clusterng so that the features we extracted for classfcaton are features of clusters returned by the mean-shft clusterng algorthm. Compared to classfcaton usng ndvdual pxel features, classfcaton by cluster features greatly reduces the dmensonalty of the problem, thus t s more effcent whle producng results wth comparable accuracy. Keywords: Image segmentaton, color space, support vector machnes, clusterng, features, tssue classfcaton, leson detecton, dgtal cervgrams, cervcal cancer 1. INTRODUCTION To make mages searchable by content n large medcal archves, t s very mportant to relably segment and label dfferent tssue regons, especally bomarker regons. We consder the automated segmentaton problem n a very large archve of 60,000 dgtzed uterne cervx mages, created by the Natonal Lbrary of Medcne (NLM) and the Natonal Cancer Insttute (NCI). These mages are optcal cervgram mages acqured by Cervcography usng specally desgned cameras for vsual screenng of the cervx, and they were collected from the NCI Guanacaste proect for the study of vsual features correlated to the development of precancerous lesons. The most mportant observaton n a cervgram mage s the Acetowhte (AW) regon, whch s caused by whtenng of potentally malgnant regons of the cervx epthelum, followng applcaton of acetc acd to the cervx surface. The cervgram mages n the archve have large varatons n ther appearance due to llumnaton varatons, artfacts n mage acquston, and ntrnsc dfferences n mage content. Exstng methods for cervgram mage analyss [1,2,3] consst of sequental steps of mage processng such as pre-processng to remove specular reflecton, segmentng

2 Fgure 1. AW vs. Non-AW Cervx color sample dstrbutons n L*a*b* space. (a) samples from one mage. (b) samples from two mages. (c) samples from three mages. (d) samples from sx mages. (red) AW color samples. (blue) Cervx color samples. cervx boundary, detecton of OS, detecton of columnar epthelum, thresholdng, and mosac texture analyss. These methods acheved good results on cervx area detecton and on fllng n specular regons, but the performance on segmentaton of mportant tssue regons such as acetowhte and columnar epthelum needs mprovement. Furthermore, due to large varatons n mage appearance, color and texture features of a tssue type n one mage often overlap wth that of a dfferent tssue type n another mage. Ths makes relable segmentaton n a large number of mages a very challengng problem. A popular color-based tssue segmentaton method s to apply clusterng technques such as K-means [3], Gaussan Mxture Models [2] and Mean-shft [6], to drectly model the posteror probabltes p(c e), where e represents an evdence vector that descrbes mage features (e.g. pxel color), and c = 1,...,C s one of the C tssue classes. One challenge facng clusterng methods n large-scale segmentaton s that color dstrbuton of one tssue class from many mages can have many modes and overlap sgnfcantly wth color dstrbutons of other tssue classes. Fgure 1 demonstrates ths problem by dsplayng Acetowhte (AW) and cervx (non-aw) color samples from 1, 2, 3, and 6 mages. Note that, as the number of mages ncreases, the AW and cervx samples ncreasngly overlap wth each other. It s therefore dffcult to predct the class of a test color sample wthout a hgh probablty of error gven assumptons about the color dstrbutons of the tssue classes. In addton, not every tssue type s always present n every mage, hence there wll lack relable ways to automatcally set clusterng parameters such as the number of clusters n K-means and GMM, and the sze of the bandwdth n Mean shft. In ths paper, we propose a database-guded segmentaton paradgm n whch we apply machne learnng technques, such as support vector machnes (SVM) to learn, from a database wth ground truth annotatons provded by experts, crtcal vsual sgns that correlate wth mportant tssue types and to use the learned classfer for tssue segmentaton n unseen mages. The support vector machnes (SVM) classfer [4, 5] has been successfully appled to detectng Mcrocalcfcatons n Mammograms [8] and varous other medcal classfcaton problems. In ths paper we use SVM to perform color-based tssue classfcaton n order to segment dfferent tssue regons, especally to segment the bomarker acetowhte (AW) regon from the rest of the cervx. The segmentaton performance s optmzed wth respect to the feature color space and granularty. We evaluated color spaces ncludng RGB, HSV, and L*a*b*. On dfferent granularty of the features, we tran AW and other tssue classfers, frst usng ndvdual pxel sample colors and then usng cluster features returned by the Mean Shft based clusterng algorthm [6]. Cluster features greatly reduce the dmensonalty of tranng so that SVM s scalable to larger tranng sets, whle producng results wth comparable accuracy. Gven a novel test mage, the Mean Shft clusterng algorthm parttons the mage nto clusters of smlar color and/or texture, and the traned SVM classfer (on cluster features of tranng data) s appled to classfyng clusters n the test mage. Ths ground-truth database guded segmentaton method s flexble n terms of the number of tssue classes. Thus we can perform ether two-label (e.g. AW vs. Non-AW cervx), or mult-label (e.g. AW, CE, SE, other) classfcaton.

3 2. METHODOLOGY For tranng and testng purposes, we have access to ground truth boundary markngs on 939 cervgram mages from the NCI/NLM archve. The ground truth markngs are collected usng a web-based Boundary Markng Tool developed by NLM and NCI [7]. There were 20 expert evaluators who used the tool to manually outlne AW and Cervx boundares n the 939 cervgrams. Some cervgrams have boundares annotated by multple experts; n ths case, we randomly choose one as the ground truth although mechansms for combnng multple-expert annotatons are avalable [9]. Fg. 1 shows some example cervgram mages wth expert annotatons. Fgure 2. Example cervgram mages wth boundary markngs by experts. the yellow outlnes cervx boundary. The blue outlnes AW regons and Fgure 3. Dstrbuton of pxel color samples n RGB (left) and L*a*b* (rght) color spaces Color Features Color and texture are two most promnent features for tssue classfcaton n dgtal cervgrams. In ths paper, we nvestgate the color features n dfferent color spaces and at dfferent granulartes. Choces of color spaces nclude the RGB, HSV, CIE L*a*b*, and others. Our experments show that lumnance (or ntensty) s an mportant feature n dscrmnatng between tssue classes, thus HSV and L*a*b* are preferred color spaces. Because of the quantzaton dscontnuty n the hue dmenson (e.g. 255 and 0 hue values are both very close to red) n the HSV color space however, for segmentaton we choose to use pxel colors n the CIE L*a*b* space. Dstrbuton of color samples n the L*a*b* space s also better for clusterng and classfcaton [6], as shown n Fg. 2.

4 Fgure 4. AW segmentaton examples. Test mage (top row), segmented AW regons (bottom row) SamplesPerImage * # of Images Tranng Tme Memory Usage 200*10 67 sec 16MB 200*20 18 mns 53MB 200*30 1 hour 34 mns 103MB 200*55 20 hours 20mns 362MB Table 1. SVM tranng tme and memory usage gven tranng sets of dfferent szes Pxel sample features vs. Cluster features Pxel color features for tranng and classfcaton A two-class pxel-wse color classfer s traned by selectng Acetowhte (AW) pxel colors n the marked ground truth AW areas as postve samples, and selectng Cervx (non-aw) pxel colors as negatve samples. 55 mages are used for tranng and 120 mages for testng. A confdence-rated SVM classfer wth a lnear kernel [4] s traned to dfferentate AW from non-aw pxel colors. Gven a test mage, the classfer s appled drectly to mage pxels, and all pxels havng the confdence rate above zero are consdered as part of the AW regon. Usng false postve fracton (FPF) and false negatve fracton (FNF) for quanttatve evaluaton, the pxel-wse classfer acheved an average of 23% FPF and 9% FNF. Some examples of AW segmentaton usng the pxel-wse classfer are shown n Fg Mean-shft clusterng and usng cluster centers for tranng and classfcaton The SVM learnng on pxel color features produces promsng AW classfcaton results as shown above. However, before SVM learners can potentally become a soluton for tssue (especally bomarker tssue) segmentaton n large medcal mage archves, we need to address the scalng problem. We record n Table 1 the processng tme and memory usage gven tranng sets of dfferent szes. One can see that, as the number of mages and/or the number of pxels from each mage for tranng ncrease, the SVM tranng tme and memory usage explode exponentally. To solve ths problem, we experment wth cluster or regon features nstead of ndvdual pxel features. For each tranng mage, we frst apply mean-shft clusterng based on L*a*b* color feature and spatal proxmty [6] to group pxels n the mage nto clusters. The label of each cluster (AW or Cervx non-aw) s assgned automatcally based on

5 Fgure 5. Labeled prmary cluster centers of a tranng mage. Feature SamplesP erimage * # of Images Preprocessng (clusterng) tme SVM Tranng Tme Memory use Pxel color 200* hours 20mns 362MB Cluster mean color 30*55 36 mns < 1 mn 12MB Table 2. Performance comparson: cluster centers as tranng data vs. pxel colors as tranng data expert boundary markngs. The cluster center, whch refers to the mean color of all pxels n the cluster, s then taken as the tranng sample. Fg. 5 shows labeled cluster centers for dfferent tssues n one tranng mage. Usng approxmately 30 cluster centers (from 15 largest AW clusters and 15 largest Cervx clusters) per mage, the tranng tme and memory cost are sgnfcantly reduced (Table 2). Gven a test mage, t s frst parttoned nto clusters usng mean shft. Then the SVM classfer learned on cluster centers s appled to classfyng each cluster n the test mage (usng the cluster center feature) to ether AW or Cervx. In our experments, the segmentaton accuracy on AW usng cluster-based classfcaton s comparable to that usng pxel-based classfcaton, whle cluster classfcaton s much more effcent and requres less memory (see Tables 1 and 2) Multple-label Classfcaton Instead of two classes (AW vs. cervx), we perform mult-label classfcaton to segment smultaneously several sgnfcant tssue regons n cervgrams ncludng the Acetowhte (AW), Columnar Epthelum (CE) and Squamous Epthelum (SE). The mult-label classfer s learned based on the one-aganst-one approach [10]. Frst, k*(k-1)/2 classfers are traned, each usng data from two dfferent classes. In a votng strategy, each bnary classfcaton by a two-class classfer s consdered to be a votng where votes can be cast for all data ponts. In the end, a data pont s

6 Fgure 6. Test result for multple tssues by usng RBF kernels of SVM. Frst s the orgnal mage; second s AW; thrd s CE. Fgure 7. Usng dfferent kernels of SVM for two-label (AW vs. cervx) classfcaton. Orgnal mage (upper left), AW by lnear kernel (upper rght), AW by polynomal wth d=3 (lower left), RBF kernel (lower rght). labeled to be n the class wth maxmum number of votes. One example output of segmented AW and CE regons s shown n Fg Kernel functon selecton of SVM We consder several kernel functon selectons for the support vector machnes classfers. Lnear: K x, x ) = x T x (. Polynomal: K( x, x ) = ( γx x + r), γ > 0. 2 Radal bass functon (RBF): K ( x, x ) = exp( γ x x ), γ > 0. Sgmod: K(, x ) = tanh( x x + r) T x γ. T

7 Our emprcal studes show that SVM classfcaton s senstve to kernel selecton, especally when we use cluster-center features because the number of samples for tranng s fewer. Fg. 7 shows an example demonstratng result dfferences by dfferent kernels. Usng cluster center features, our experments show that the RBF kernel outperforms others n two-label classfcaton, whle the lnear kernel s the best n mult-label classfcaton. Usng ndvdual pxel color features, the segmentaton accuracy on most tssues, such as AW, CE, and SE, s comparable by dfferent kernels on most test mages. 3. CONCLUSIONS AND DISCUSSIONS We ntroduce a database guded dscrmnatve approach to segmentng tssue, especally bomarker acetowhte tssue, regons n dgtzed uterne cervx mages. Tranng a support vector machne (SVM) classfer usng cluster center features gves us better effcency than usng ndvdual pxel features due to the reduced dmensonalty whle producng comparable accuracy. The method can be extended to segmentng other sgnfcant tssue regons n cervgrams ncludng the Columnar Epthelum (CE) and Squamous Epthelum (SE) usng multple label classfcaton. Comparng dfferent kernel functons for the support vector machnes classfer, we fnd that, wth cluster features, the lnear kernel s more sutable n mult-label classfcaton whle the Radal Bass Functons kernel s better for two-label classfcaton. ACKNOWLEDGMENTS We would lke to thank the Communcatons Engneerng Branch, Natonal Lbrary of Medcne, and the Natonal Cancer Insttute for provdng the data and support of ths work. REFERENCES [1] Zmmerman G. and Greenspan, H., Automatc detecton of specular reflectons n uterne cervx mages, Proc. of SPIE Medcal Imagng, volume 6144, pages (2006) [2] Gordon S., Zmmerman, G., Long, R., Antan, S., Jeronmo J. and Greenspan, H., Content Analyss of Uterne Cervx Images: Intal steps towards content based ndexng and retreval of cervgrams, Proc. of SPIE medcal magng, volume 6144, pages (2006) [3] Tulpule, B., Hernes, D., Srnvasan, Y., Yang, S., Mtra, S., Srraa, Y., Nutter, B., Phllps, B., Long, L.R. and Ferrs, D., A probablstc approach to segmentaton and classfcaton of neoplasa n uterne cervx mages usng color and geometrc features, Proc. of SPIE Medcal Imagng, Volume 5747, pages (2005) [4] Joachms, T., Makng large-scale SVM Learnng Practcal, Advances n Kernel Methods - Support Vector Learnng, B. Schölkopf and C. Burges and A. Smola (ed.), MIT Press (1999) [5] Vapnk, V., Statstcal Learnng Theory, Wley-Interscence (1998) [6] Comancu, D. and Meer, P., Mean shft: A robust approach toward feature space analyss, IEEE Transacton on Pattern Analyss and Machne Intellgence, Vol. 24, No. 5 (2002) [7] Jeronmo, J., Long, L., Neve, L., Bopf, M., Antan, S. and Schffman, M., Dgtal tools for collectng data from cervgrams for research and tranng n colposcopy, J. of Lower Gental Tract Dsease 10(1) (2006) [8] El-Naqa, I., Yang, Y., Wernck, M. N., Galatsanos, N. P. and Nshkawa, R. M., A Support Vector Machne Approach for Detecton of Mcrocalcfcatons," IEEE Trans. on Medcal Imagng, 21(12): (2002)

8 [9] Warfeld, S. K., Zou, K. H. and Wells, W. M., Smultaneous Truth and Performance Level Estmaton (STAPLE): An Algorthm for the Valdaton of Image Segmentaton," IEEE Trans. on Medcal Imagng, 23(7): (2004) [10] Knerr, S., Personnaz, L. and Dreyfus, G., Sngle-layer learnng revsted: a stepwse procedure for buldng and tranng a neural network," In J. Fogelman, edtor, Neu-rocomputng: Algorthms, Archtectures and Applcatons. Sprnger-Verlag (1990)