A Computer-aided System for Discriminating Normal from Cancerous Regions in IHC Liver Cancer Tissue Images Using K-means Clustering*

Transcription

1 A Computer-aded System for Dscrmnatng Normal from Cancerous Regons n IHC Lver Cancer Tssue Images Usng K-means Clusterng* R. M. CHEN 1, Y. J. WU, S. R. JHUANG, M. H. HSIEH, C. L. KUO, Y. L. MA Department of Computer Scence and Informaton Engneerng Natonal Unversty of Tanan 33 Sec. 2, Shuln St., Tanan TAIWAN 1 rmchen@mal.nutn.edu.tw R. M. HU 2 and JEFFREY J. P. TSAI 3 Department of Bomedcal Informatcs Asa Unversty 500 Loufeng Rd., Wufeng, Tachung TAIWAN 2 rmhu@asa.edu.tw 3 jjptsa@gmal.com Abstract: - Immunohstochemstry (IHC) s a well establshed magng technque that can be exploted to detect whether the target antgen exsts n tssue sectons or not n order to dscrmnate between the cancerous and normal regons n a cancer tssue specmen. The ntensty of mmuno-staned proten n normal and cancerous regons can be compared to detect the gene status n sample tssues. In ths paper, we address the problem of dentfyng the dfferental expresson of marker proten on cancerous and normal regons n an IHC lver cancer tssue mage. We present an mproved IHC mage processng procedure based on nucleus densty, ntensty of staned proten, and k-means clusterng algorthm to develop an automated system for analyzng an IHC mage. The proposed system can dscrmnate between normal and cancerous regons n an IHC mage more effectvely and dsplay them vsually. Furthermore, ths system can automatcally evaluate the staned proten expressons n the two regons whch can help the pathologst to analyze the dfferental expressons of a marker proten from IHC mages. Fnally, we evaluated the proposed system on 150 real IHC lver cancer tssue mages and compared the results wth those obtaned usng support vector machne (SVM) and a prevous work where the average of densty of nucle s used as the threshold to dscrmnate cancerous from normal regons. Key-Words: - Immunohstochemstry, Tssue Image, Nucle Segmentaton, K-means Clusterng, Support Vector Machne 1 Introducton It was generally beleved that the formaton of cancer s due to actvaton of oncogenes or n actvaton of tumor suppressor genes. Many studes of genes assocated wth cancer have been conducted over the years, expected to fnd genes that cause cancer and genes that can nhbt cancer [1]. Several approaches have been developed to help dagnose cancer n the past. These approaches may nclude the use of ultrasound, magnetc resonance, and computerzed topography scan, etc. However, the dagnoss of cancer should eventually be confrmed subject to the bopsy. Wth the advances of mcroarray technology, mcroarray technology can be used to determne molecular markers and the type of cancer [2]. However, mcroarray gene expressons are usually only able to provde early stage nformaton of gene * An earler verson of ths paper was presented at The Conference on Technologes and Applcatons of Artfcal Intellgence (Domestc Track), Natonal Chao Tung Unversty, Hsnchu, Tawan, Nov. 19, E-ISSN: Volume 11, 2014

2 expressons. The dfferental gene expressons are stll needed to be valdated by quantfed polymerase chan reacton (PCR). A complementary magng technque to quantfy proten actvty s based on mmunohstochemstry (IHC) mages [3]- [9]. Whle a number of automated or semautomated technques are ncreasng n popularty, manual nterventon s stll necessary n order to resolve partcularly challengng or ambguous cases. Ths calls for new computer-aded tools to assst the pathologst n the examnatons of ther work. For ths, an extensve revew for automated analyss of IHC mages has been made by Theodosou et al. [10]. IHC can be used to detect whether the target antgen exsts n tssue sectons or not n order to dscrmnate between the cancerous and normal regons n a cancer tssue specmen. It can help us to recognze the locaton and dstrbuton of marker protens n dfferent regons of the specmen. However, f the boundares of normal and cancerous regons are not clear, the analyss results wll be extremely affected by nter-observer varablty [11]. Moreover, t s tme-consumng to extract nformaton from very large datasets by manual analyss. Fg. 1 shows an example of IHC mage of lver cancer tssue. Ths knd of mages wll be taken as the target mage to be consdered n ths paper. The dark blue parts of the mage are nucle. The densty of nucle can be used to dstngush between normal and cancerous regons n the tssue. The regon wth a hgher densty of nucle wll be recognzed as cancerous tssue and the regon wth a lower densty of nucle wll be recognzed as normal tssue. However, because tssues may have some deformaton when embeddng and cells are not neatly arranged n the same plane, many nucle may be seen to be connected vsually from the bopsy. Besdes, snce the composton of human lver cell s a cell wth multple nucle, t does not necessarly have a nuclear membrane between nucle. Hence, countng the number of nucle per unt regon s not an effectve way to fnd the densty of nucle. One way to deal wth ths problem s to dscrmnate nucle regon from non-nucle regon based on the color nformaton of nucleus before calculatng the densty of nucle [12]. Another factor that wll affect the accuracy of dscrmnaton s the threshold for the densty of nucle. In ths paper, we wll consder ths ssue based on the k-means clusterng algorthm. The k-means clusterng has been a well-known unsupervsed learnng algorthm [13]. D Cataldo et al. [14] have also demonstrated that the unsupervsed approach, namely k-means clusterng, overcomes the accuracy of a theoretcally superor supervsed method such as Support Vector Machne (SVM) [15] based on the expermental results on a heterogeneous dataset of IHC lung cancer tssue mages. We wll propose an mproved IHC mage processng procedure based on nucleus densty, staned proten expressons, and k-means clusterng algorthm to develop an automated system for dscrmnatng normal from cancerous regons n IHC lver cancer tssue mages. Expermental results on 150 real IHC mages demonstrate that the proposed system acheves better results as compared to those obtaned usng support vector machne and a prevous work where the average of densty of nucle s used as the threshold to dscrmnate cancerous from normal regons [12]. Fg. 1. An example of IHC mage of lver cancer tssue. 2 Method In ths secton, we ntroduce the methods and an mproved automated system for IHC lver cancer mage analyss. The work flow of the mproved system s shown n Fg. 2. The work flow s manly conssted of four steps as descrbed below. 2.1 Recognzng nucleus and staned proten regons The objectve of the frst step of IHC mage analyss s to recognze nucleus and staned proten regons n the IHC lver cancer tssue mage. The nucle appear as dark blue color n the IHC mage as shown n Fg. 1. Instead of usng color deconvoluton to deal wth the effect of stans colocalzaton [9], we transform the orgnal color IHC mage acqured wth a red-green-blue (RGB) camera to YIQ color space [16]. The reason to use YIQ color representaton s that t takes advantage of human color-response characterstcs. The Y component stands for the luma nformaton (the E-ISSN: Volume 11, 2014

3 brghtness n an mage). The I and Q components stand for the chromnance nformaton wth color n the orange-blue range and purple-green range, respectvely. We extract the I component of the mage and enhance t wth a threshold selecton method [17]. Then, the B component of the orgnal RGB color mage s replaced by the enhanced I component of the converted YIQ color mage. Snce our goal here s to dentfy the regons of nucleus, we use the followng formula to transform the mage to gray-level mage: Y 0.299R 0.587G 0.114B, 1, 2,, N (1) where Y denotes the gray level of pxel and N the number of pxels of the mage. R, G, and B represent the R, G, and B components of pxel, respectvely. Ths gray-level mage s then converted to bnary mage. The segmented black color regons of the resultng bnary mage denote the nucle. After recognzng the nucleus, we dentfy the regon of staned proten n the orgnal IHC mage, where the brown staned regons ndcate that the marker proten s present. of sze n x n, whch can be defned by the user. The average nucleus densty at segment, DN s calculated by the followng formula [12]: C DN, 1, 2,, block _ number (2) A where C s the number of pxels of nucleus regons at segment of the mage, A s the number of pxels of segment, and block_number s the number of segments of the mage. The average nucleus densty at each segment s selected as the dscrmnant feature. After evaluatng the average nucleus densty of all segments, we apply the k-means clusterng to separate the cancerous from normal mage regons based on the dscrmnant feature. In statstcs and machne learnng, k-means clusterng s an approach of cluster analyss whch ams to separate observatons nto k clusters such that each observaton s assgned to the cluster wth the nearest mean; t s also referred to as Lloyd's algorthm [18]-[20]. Snce the k-means clusterng s a heurstc algorthm, there s no guarantee that t wll converge to the global optmum, and the result may depend on the ntal data. Usually, the ntal data are specfed at random or by some heurstc data. In the proposed system, we wll sort the nucleus densty of all segments at frst, and then the maxmum densty and mnmum densty are assgned to the ntal data. Ths settng makes the clusterng result more accurate n our analyss. Another problem wth usng k-means clusterng algorthm s to determne the number of clusters, an napproprate choce of k may yeld poor results. The number of k clusters s set to 2 snce only two classes, normal and cancerous tssue regons, are to be separated n ths analyss. Fg. 2. Work flow of the proposed system for IHC lver cancer mage analyss. 2.2 Dscrmnatng cancerous from normal regons usng k-means clusterng The purpose of ths step s to dstngush between normal and cancerous tssue regons n the IHC mage based on the nformaton of nucleus densty. The mage s frst segmented usng a sldng wndow 2.3 Computng the ntensty of staned proten n cancerous and normal regons After dscrmnatng cancerous from normal regons n an IHC tssue mage, the thrd step s to compare the ntensty of staned proten n both regons n order to detect the gene status n sample tssues. We frst map the regons of staned proten obtaned n Secton 2.1 onto cancerous and normal regons of the IHC mage. The average ntensty of staned proten n cancerous regon, denoted by DP_CR, s then evaluated by the followng formula: num_ cr j1 ( x, CAj num _ cr Aj Cj DP_ CR f ( x, (3) j1 E-ISSN: Volume 11, 2014

4 where num_cr s the number of segments n cancerous regon, A j s the number of pxels of j th segment n cancerous regon, C j s the number of pxels of nucleus regon n j th segment of cancerous regon, f(x, s the gray-level ntensty at coordnate (x,, and CA j s the set of coordnates n j th segment of cancerous regon. Note that the average ntensty of staned proten s calculated by excludng the regon of nucle n each segment. Smlarly, the average ntensty of staned proten n normal regon, denoted by DP_NR, can be evaluated by the followng formula: num _ nr j1 ( x, NA j _ nr B j N j DP_ NR f ( x, (4) num j1 where num_nr s the number of segments n cancerous regon, B j s the number of pxels of j th segment n normal regon, N j s the number of pxels of nucleus regon n j th segment of normal regon, f(x, s the gray-level ntensty at coordnate (x,, and NA j s the set of coordnates n j th segment of normal regon. 2.4 Detectng the gene status n sample tssues The fnal step of the proposed system s to detect the gene status n sample tssues based on the comparson of average ntensty of staned proten n cancerous and normal regons obtaned n Secton 2.3. There are three gene statuses for the detecton. If the average ntensty of staned proten n cancerous regon s greater than the one n normal regon, the gene status wll be recognzed as "Over Expresson". If the average ntensty of staned proten n cancerous regon s less than the one n normal regon, the gene status wll be recognzed as "Under Expresson". Otherwse, the gene status wll be recognzed as "no sgnfcant dfference" whch may mply the target marker proten should have no relatonshp wth the target dsease. In the case of "no sgnfcant dfference", the proposed system also provdes a user-defned threshold for dstngushng the average ntensty of staned proten between cancerous and normal regons. 3 Performance measure In ths secton, we ntroduce the performance measure used to evaluate the success of the proposed method. The success of a test les n ts ablty to retrevng correct results for a gven task. In statstcs, the F-score (also F-measure) s a measure for evaluatng the performance of a test [21]. It consders both the precson p and recall r of the test to compute the score. The precson s defned as the fracton of tested results that are correct. The recall s defned as the fracton of correct results that are tested. In these terms, F-score s defned as the weghted harmonc mean of recall and precson. The formula used to compute the F-score here s as follows: rp F 2 (5) r p In the followng secton, we wll use the F-score to evaluate the detectng performance of the proposed method and the method of prevous work n [12]. 4 Expermental results and dscusson The proposed system was mplemented n Borland C++ Bulder, a well-known C++ development envronment, and combned wth the MATLAB. In ths paper we have carred out an extensve expermental evaluaton on 150 real IHC mages [22] for three methods. The frst method s the proposed system wth k-means clusterng (desgnated Method 1), the second one s SVM (desgnated Method 2) and the thrd one s a prevous work [12] (desgnated Method 3) usng the average of densty of all nucle as the threshold to dscrmnate cancerous from normal regons. In Method 2, we frst cut each IHC mage nto 64 blocks wth each block sze set to 32 x 32.Then we have a total of 9600 blocks of submages. Among these submages, we choose 1000 submages wth random for tranng, ncludng 250 blocks of cancerous submages and 750 blocks of normal submages. The remanng 8600 submages are used as the test dataset for classfcaton. The comparson of performance measures, average F-score and accuracy, are shown n Table 1. The average F-score obtaned usng Method 1, Method 2, and Method 3 s 82.49%, 56.77%, and 67.71%, respectvely. It can be found that Method 1 overcomes the average F-score of Method 2 and Method 3 by 25.72% and 14.78%, respectvely. On the other hand, Method 1 acheves an average accuracy of 89.84%, Method 2 acheves an average accuracy of 77.99%, and Method 3 acheves an average accuracy of 77.95%. Method 1 also overcomes the average accuracy of Method 2and Method 3 by 11.85% and 11.89%, respectvely. The overall performance of Method 1 s better than that of Method 2 and Method 3 based on ther average F-score and accuracy. E-ISSN: Volume 11, 2014

5 Table 1. Performance Comparsons of Method 1, Method 2, and Method 3. Method F-score (%) Accuracy (%) Method 1 (k-means) Method 2 (SVM) Method 3 (Average) A snapshot of graphcal user nterface (GUI) of the proposed system s shown n Fg. 3. The mages shown from left to rght are orgnal mage, nucleus mage, mage wth user-defned cancerous segments, and mage wth detected cancerous segments, respectvely. The assumed cancerous regons n the mage wth user-defned cancerous segments can be specfed by the human expert va the button Settng Cancerous Segments n the GUI. The sze of sldng wndow for mage segmentaton s set to 32 x 32 n our experments. The performance for the detecton of cancerous segments can be evaluated by clckng the button F-score (see Secton IV for detals about the F-score). A snapshot of an llustratve example obtaned wth Method 1 on one of the real IHC mage s shown n Fg. 4. It can be seen from Fg. 4 that Method 1 acheved an F-score of 0.8 (or 80%) and obtaned a result of Over Expresson for the estmated gene status. 5 Concluson We presented an mproved computer-aded analyss system of IHC lver cancer tssue mages. The procedure was manly conssted of four steps, namely recognzng nucleus and staned proten regons, dscrmnatng cancerous from normal regons usng k-means clusterng, computng the ntensty of staned proten n cancerous and normal regons, and detectng the gene status n sample tssues. The proposed system was tested on 150 real IHC lver cancer tssue mages. Expermental results demonstrated the hgh average F-score and accuracy achevable by the proposed system compared to the method of SVM and a prevous work where the average of densty of nucle s used as the threshold to dscrmnate cancerous from normal regons. Acknowledgements Ths work was supported n part from the Natonal Scence Councl, Tawan [grant numbers: NSC E and NSC E ]. Fg. 3. A snapshot of graphcal user nterface (GUI) of the proposed system. Fg. 4. A snapshot of expermental results obtaned wth Method 1 on one of the real IHC mage. References: [1] S. Veerla, Cancer-Related Gene Regulaton Mechansms: Cancer Genetcs, Transcrpton Factors, Gene Regulaton Mechansms, Bonformatcs, VDM Verlag, [2] J.A. Warrngton, R. Todd, D. Wong, Mcroarrays and cancer research, Eaton Publshng Company, [3] E.M. Breya, Z. Lalanc, C. Johnstona, M. Wongc, L. V. McIntreb, P. J. Duked, C. W. Patrck, Automated selecton of DAB-labeled tssue for mmunohstochemcal quantfcaton, Journal of Hstochemstry and Cytochemstry, Vol.51, 2003, pp [4] J. K. Cho, U. Yu, O. J. Yoo, S. Km, Dfferental coexpresson analyss usng mcroarray data and ts applcaton to human cancer, Bonformatcs, Vol.21, 2005, pp [5] S. D Cataldo, E. Fcarra, A. Acquavva, E. Mac, Achevng the way for automated segmentaton of nucle n cancer tssue mages through morphology-based approach: A quanttatve evaluaton, Computerzed Medcal Imagng and Graphcs, Vol.34, No.6, 2010, pp [6] S. D Cataldo, E. Fcarra, A. Acquavva, E. Mac, Automated segmentaton of tssue E-ISSN: Volume 11, 2014

6 mages for computerzed IHC analyss, Computer Methods and Programs n Bomedcne, Vol.100, No.1, 2010, pp [7] E. Fcarra, E. Mac, L. Benn, G. De Mchel, Computer-aded evaluaton of proten expresson n pathologcal tssue mages, n: 19th IEEE Symposum on Computer-Based Medcal Systems, 2006, pp [8] J. T. Jrgensena, Pharmacodagnostcs and targeted therapes: a ratonal approach for ndvdualzng medcal antcancer therapy n breast cancer, Oncologst, Vol.12, 2007, pp [9] A. C. Rufrok, D.A. Johnston, Quantfcaton of hstochemcal stanng by color deconvoluton, Anal. Quant. Cytol. Hstol., Vol.23, 2001, pp [10] Z. Theodosou, I. Kasampalds, G. Lvanos, M. Zervaks, I. Ptas, K. Lyrouda, Automated analyss of FISH and mmunohstochemstry mages: a revew, Cytometry, Part A, Vol.71, 2007, pp [11] M. Lacrox-Trk, S. Mathouln-Pelsser, J. Ghnassa, G. Macgrogan, A. Vncent-Salomon, V. Brouste, M. Matheu, P. Roger, F. Bbeau, J. Jacquemer, Hgh nter-observer agreement n mmunohstochemcal evaluaton of HER- 2/neu expresson n breast cancer: a multcentre GEFPICS study, EJC, Vol.42, 2006, pp [12] C. Y. Hsu, R. M. Hu, R. M. Chen, J. W. Ou, J. P. Tsa, IHCREAD: an automatc mmunehstochemstry mage analyss tool, n: Bomedcal Engneerng: Health Care Systems, Technology and Technques, Sprnger, New York, [13] A. K. Jan, R. C. Dubes, Algorthms for clusterng data, Prentce Hall, Englewood Clffs, [14] S. D Cataldo, E. Fcarra, E. Mac, Automated dscrmnaton of pathologcal regons n tssue mages: unsupervsed clusterng vs. supervsed SVM classfcaton, Commun. Comput. Inform. Sc., 2008, pp [15] V. Vapnk, Statstcal Learnng Theory, Wley- Interscence, New York, [16] J. D. Foley, A. van Dam, S. K. Fener, J. F. Hughes, Computer Graphcs: Prncples and Practce, Readng, MA: Addson-Wesley, [17] N. Otsu, A threshold selecton method from gray-level hstograms, IEEE Trans. Systems, Man, and Cybernetcs, Vol.9, 1979, pp [18] J. A. Hartgan, M. A. Wong, Algorthm AS 136: a k-means clusterng algorthm, Journal of the Royal Statstcal Socety, Seres C (Appled Statstcs), Vol.28, 1979, pp [19] S. Lloyd, Least squares quantzaton n PCM, IEEE Trans. Informaton Theory, Vol.28, 1982, pp [20] J. B. MacQueen, Some methods for classfcaton and analyss of multvarate observatons, n: 5th Berkeley Symposum on Mathematcal Statstcs and Probablty, Vol.1, 1967, pp [21] C. J. van Rjsbergen, Informaton Retreval, Butterworth, London, [22] J. W. Lu, J. G. Chang, K T. Yeh, R. M. Chen, Jeffrey J. P. Tsa, W. W. Su, R. M. Hu, Increased expresson of PRL-1 proten correlates wth shortened patent survval n human hepatocellular carcnoma, Clncal and Translatonal Oncology, Vol.14, 2012, pp E-ISSN: Volume 11, 2014