FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION

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1 ISSN Internatonal Scentfc Journal of Computng FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION Gábor Takács ), Béla Patak 2) Department of Measurement and Informaton Systems, Budapest Unversty of Technology and Economcs, H-7 Budapest, Magyar tudósok körútja 2. ) gtakacs@mt.bme.hu, 2) patak@mt.bme.hu, Abstract: Breast cancer s one of the most common forms of cancer among women. Currently mammography s the most effcent method for early detecton. A smple and fast mammographc mass detecton system and two dfferent methods for dffcult case excluson are presented n ths paper. The mass detecton system uses a modfed verson of a known algorthm for small masses and a new algorthm for large masses. The frst dffcult case flterng method s based on tssue densty estmaton, the second one on mass canddate count. The system was tested wth 600 mammographc cases, each contanng 4 mages. Case-level performance was measured for malgnant mass detecton frst wthout and then wth dffcult case excluson. Keywords: Mammography, Mass Detecton, Image Processng, Computer-Aded Dagnoss. INTRODUCTION Breast cancer s one of the most frequent cancerous dseases among women. Every 2th woman suffers from ths dsease at least once n her lfetme []. Snce the cause of the dsease s unknown, early detecton s very mportant. Currently mammography (X-ray examnaton of the breast) s the most effcent method for early detecton. In a mammographc sesson usually two mages are taken of both breasts. Left cranocaudal (LC) s a top vew, left medolateral (LM) s roughly a sde vew mage of the left breast, rght cranocaudal (RC) and rght medolateral (RM) are the same vews of the rght breast. Wth regular mammographc screenng examnatons the mortalty of the dsease can be sgnfcantly decreased. (If breast cancer s detected early, the fve-year survval rate exceeds 95 %.) The evaluaton of the mages taken at the screenng examnatons needs a large amount of human resource and money. Therefore computeraded dagnoss (CAD) for mammography has been an actve area of research (e.g. [2], [3]). The man goals of a CAD system are to ncrease the accuracy of examnaton by amng radologsts' attenton to suspcous cases and to decrease the cost by flterng out normal cases. The most mportant mammographc symptoms of breast cancer can be dvded nto two man classes: Mcrocalcfcaton: group of small whte calcum spots. Mass: crcumscrbed object brghter than ts surroundng tssue. Not all mcrocalcfcatons and masses are cancerous, they can also be bengn. The two man classes can be dvded nto subclasses, for example the ACR (Amercan Collage of Radology) BI- RADS recommendaton [4] defnes 9 mass and 3 mcrocalcfcaton subtypes. Combned massmcrocalcfcaton lesons are possble too. The recognton of these structures s a hard and challengng task that needs ntellgence. Mass detecton s partcularly dffcult, because masses show a great dversty n optcal densty, shape, poston, sze and characterstcs at the edge. Humans and computer algorthms have to deal wth a number of dffcultes: for example the boundary can be fuzzy or partally mssng, rrelevant objects can overlap the mass, some bengn fndngs (e.g. cysts) also appear as masses, normal archtectural structures of the breast supermposed on each other can look lke real masses. Fgures 4 show some typcal forms of mcrocalcfcatons and masses appearng n real mammograms. 70

2 Fg. A typcal bengn mcrocalcfcaton Fg. 2 A typcal malgn. mcrocalcfcton values and the average of those calculatons yelds the average FUM (AFUM) value. Ths AFUM algorthm varant slghtly dffers from the orgnal one, because n the orgnal algorthm the mnmal ntensty at dstance less than or equal r from (x, y) s compared to ntensty values at dstance r 2 from (x, y). In real mammograms some masses contan small dark dots nsde. The orgnal AFUM algorthm prohbts ths case whle the proposed varant tolerates t to some degree. If r = R mn, R mn, R mn 2,..., R max and r 2 = r D then the AFUM value calculaton can be wrtten as: R max R mn max r = R R mn FUM ( r, r D) () Fg. 3 A typcal bengn mass Fg. 4 A typcal malgnant mass Ths paper presents a smple and fast system for detectng masses n dgtalzed mammograms. Two sze classes were defned and dfferent algorthms are used for small and large mass detecton. The precson of the system was mproved by applyng dffcult case excluson. A tssue densty estmaton based and a mass canddate count based method were developed and tested n the dffcult case flterng experments. 2. DETECTION OF SMALL MASSES The sze of mammographc masses vares n a wde range (~5 mm to ~50 mm n dameter). An nterestng queston of automated breast cancer detecton s how to handle ths sze varablty. The proposed system defnes two sze classes and uses dfferent algorthms for small and large mass detecton. Most mammographc masses belong to the small class so the small mass detector s the crtcal part of the system. Large masses are rare and easer to detect. For the detecton of small masses (smaller than 20 mm n dameter) a slghtly modfed verson of the AFUM mass detecton algorthm [5] was appled. A short descrpton of our modfed algorthm: At each pxel poston (x, y) the mnmal ntensty at dstance r from locaton (x, y) s computed (m ), then the fracton of pxels at dstance r 2 from (x, y) that have lower ntensty value than m s measured. Ths fracton under the mnmum (FUM) calculaton s done over many scales usng a range of r and r 2 Values R mn R max and D are fxed a pror choces based on the problem defnton. An advantage of ths algorthm s smplcty and therefore computatonal effcency. A nce property of the algorthm s nvarance to any monotoncally ncreasng ntensty transformaton of the nput mage, snce only logcal operators (mn. fndng and comparson) are appled to the ntensty values. A fast mass detector can be obtaned by runnng the AFUM algorthm for each non-background pxel of a mammogram. The fltered mage s thresholded and contnuous regons are dentfed by a regonfllng algorthm. Regons wth a too hgh permeterarea rato are excluded from further examnatons. The locaton of the maxmal AFUM value s computed for each regon, and an energy value s assgned for each maxmum locaton based on the AFUM value of that poston and ts neghborng pxels. A structure s accepted as a mass f ths energy s hgher than a lmt. Fnally the locatons of the hghest N energy maxma are returned. Fgure 5 llustrates the steps of small mass detecton: Fg. 5 The steps of small mass detecton 7

3 3. DETECTION OF LARGE MASSES The a pror parameters of the AFUM algorthm (R mn, R max and D) could not be set to deal wth arbtrary mass sze. At the resoluton of 400 mcrons R mn = 0, R max = 6 and D = 2 proved to be a good choce but worked well only for masses smaller than 20 mm n dameter. For the detecton of larger masses the followng smple and fast algorthm was developed: At a gven pxel poston 8 lnes are started from the center (vertcally, horzontally and dagonally) and a mass boundary pont s estmated for each drecton based on some smple ntensty change constrants. Then a conspcuousness value can be obtaned from the lne lengths (l ), average ntensty along the lnes (Brghtness) and average contrast at the end of the lnes (Contrast). Conspcuousness = mn l Brghtness Regularty Contrast (2) Regularty = mnl maxl (3) Snce the detecton of large masses s easer than that of the small ones, the large mass detector returns only the locaton of the hghest conspcuousness value when processng a whole mammogram. Obvously a lower threshold for the conspcuousness can be appled to control the senstvty of the large mass detector. 4. DIFFICULT CASE EXCLUSION BASED ON TISSUE DENSITY ESTIMATION Breast tssue densty s an mportant feature of a mammographc case for human experts. There are standards for classfyng mammogaphc cases nto tssue densty classes. For example BI-RADS recommends four breast tssue densty types numbered from to 4. Class means fatty tssue (dark, homogenous background), class 4 means dense tssue (that can mask nterestng structures). Human experts say that mammographc mass detecton (and mcrocalcfcaton detecton) s a much more dffcult task on dense cases than on fatty ones. For cases wth an extremely dense tssue mammogaphy s not applcable at all. Fgures 6 7 show a fatty and a dense case, each contanng a malgnant mass. Fg. 6 A fatty case contanng a malgnant mass Fg. 7 A dense case contanng a malgnant mass 72

4 Accordng to ths observaton a densty estmaton algorthm was developed to mprove the accuracy of the mass detector by flterng out dffcult cases. The densty estmator measures the ntensty mean and varance of the four mages (LC, LM, RC and RM) of the nput case. Then the estmated densty value s computed wth the followng heurstc formulae: EstmatedDensty = ( 2 NSumSd NSumMean) / NSumSd = ( RC RM LC LM 66.8) / 24.5 (4) (5) NSumMean = ( Mean Mean RC Mean RM LC Mean LM 492.6) / 73.0 (6) NSumSd denotes statstcally normalzed sum of the standard devatons, NSumMean denotes statstcally normalzed sum of the means. Constants are set to make the estmated densty value drectly comparable wth the BI-RADS densty value. Experments showed that our estmated densty value (rounded to the nearest nteger) usually does not equal exactly wth the BI-RADS densty value, but the two parameters are n postve correlaton wth each other (Fgure 7). The measurement was based on 57 mammographc cases of the DDSM database [6]. Fg. 8 The correlaton between the BI-RADS and the estmated densty value 5. DIFFICULT CASE EXCLUSION BASED ON MASS CANDIDATE COUNT The frst dffcult case flterng method was developed accordng to a human experts observaton: dense cases are more dffcult than fatty ones. Another possble way s to get the case dffculty nformaton from the mass detecton system tself. For example the number of energy maxma after the permeter-area rato flterng step n the small mass detector (mass canddate count) seems to be a good parameter. If the number of energy maxma s hgh then real masses have lesser chance to be among the N hghest maxma, so the flm s dffcult for the system. The case-level mass canddate count value can be obtaned by the summaton of flmlevel mass canddate counts. The average mass canddate count of the BI- RADS densty classes can be seen n Table (based on 57 cases of the DDSM database [6]). 73

5 Table. Average mass canddate count of the BI- RADS densty classes BI-RADS Densty Avg. Mass Canddate Count 6. RESULTS The mass detecton system was tested wth 600 cases (600 4 = 2400 mages) of the DDSM database [6]. 424 cases contaned malgnant masses, 0 cases contaned bengn (but no malgnant) masses, 66 cases contaned no masses. A malgnant case was counted as recognzed f one of the pxel postons returned by the mass detector was nsde the radologst-drawn boundary of a malgnant mass on any mage of the case. An output pxel poston was counted as a false mark (FM) f t was not nsde the radologst-drawn boundary of any (malgnant or bengn) mass. Table 2 shows the performance parameters of the system wthout dffcult case excluson: Table 2. Results of the mass detector MCRR # FMs / Image 90.3 % 5.45 The malgnant case recognton rate (MCRR) s nearly 90 % that s acceptable n tself, but t comes together wth a hgh number of false marks per mage. At ths false mark level the system can be used for ncreasng the accuracy of the examnaton but cannot be used for decreasng the cost by flterng out normal cases. Results wth densty estmaton based dffcult case excluson are summarzed n Table 3: Table 3. Results wth densty estmaton based dffcult case excluson Rejecton Rate MCRR # FMs / Image 8.7 % 90.4 % % 90.6 % % 90.8 % % 90.6 % % 92.9 % 5.45 The false mark level remaned nearly the same. The malgnant case recognton ncreases wth the rejecton rate but a sgnfcant mprovement can be observed only at a very hgh rejecton rate. Table 4 shows the results of mass canddate count based dffcult case excluson: Table 4. Results wth mass canddate count based dffcult case excluson Rejecton Rate MCRR # FMs / Image 4.2 % 90.8 % % 9.3 % % 9.8 % % 9.8 % % 93.9 % 5.36 The false mark level s slghtly reduced. The malgnant case recognton rate ncreases monotoncally wth the rejecton rate. The mass canddate count based flterng provdes a better malgnant case recognton than the densty estmaton based one at the same rejecton rate. The system performs better than ts prevous verson [7] from whch the large the mass detector part was mssng (Table 5). Table 5. Some results of the prevous verson of the system Rejecton Rate MCRR # FMs / Image 0 % 89.6 % % 88.7 % 5.5 (densty est. based flterng) 4.3 % (mass count based flterng) 90.3 % CONCLUSION A mammographc mass detecton system and two algorthms for dffcult case excluson were presented n ths paper. The system uses dfferent methods for small and large mass detecton. The frst dffcult case flterng algorthm s based on tssue densty estmaton, the second one on mass canddate count. The mass detector s fast enough to process each pxel of mammogram n reasonable tme at the resoluton of 400 mcrons. It fnds almost all malgnant masses, but also returns a hgh number of false marks. The malgnant case recognton rate of the mass detector was mproved by flterng out dffcult cases. The mass count based dffcult case flterng proved to be better than the tssue densty estmaton based method. Further mprovements could be acheved by mplementng a complex post-processng for the suspcous spots returned by the fast mass detector, and by comparng the two vews of the same breast. 74

6 8. REFERENCES [] R. Hghnam, M. Brady (Edtors). Mammographc Image Analyss, Kluwer Academc Publshers, 999. [2] S. Lee, P. Chung, C. Chang, C. Lo, T. Lee, G. Hsu, C. Yang. Classfcaton of Clustered Mcrocalcfcatons Usng a Shape Cogntron Neural Network. Neural Networks 6 (2003), pp [3] M. Altrchter, Z. Ludány, G. Horváth. Jont Analyss of Multple Mammographc Vews n CAD Systems for Breast Cancer Detecton. Proceedngs of the 4th Scandnavan Conference on Image Analyss (SCIA2005), Joensuu, Fnland, 2005, pp [4] Amercan College of Radology. Illustrated Breast Imagng Reportng and Data System (BI- RADS) (3rd ed). Reston, VA: Amercan College of Radology, 998. [5] M. D. Heath, K. W. Bowyer. Mass Detecton by Relatve Image Intensty. Proceedngs of the Ffth Internatonal Workshop on Dgtal Mammography (IWDM-2000), Toronto, Canada, 2000, pp [6] M. D. Heath, K. W. Bowyer, D. Kopans. Current status of the Dgtal Database for Screenng Mammography. Dgtal Mammography, Kluwer Academc Publshers, 998, pp [7] G. Takács, B. Patak. Fast Detecton of Mammographc Masses wth Dffcult Case Excluson. Proceedngs of the Thrd IEEE Workshop on Intellgent Data Acquston and Advanced Computng Systems: Technology and Applcatons (IDAACS 2005), Sofa, Bulgara, 2005, pp Gábor Takács, Born n Győr, Hungary, receved M.Sc. degree n techncal nformatcs from the Budapest Unversty of Technology and Economcs n He s currently a Ph.D. student at the Department of Measurement and Informaton Systems, Budapest Unversty of Technology and Economcs. Hs research nterests nclude pattern recognton, machne learnng and medcal mage processng. Béla Patak, Born n Budapest, Hungary, receved M.Sc., Techncal Doctorate and Ph.D. degrees n electrcal engneerng from the Budapest Unversty of Technology and Economcs, n 978, 994 and 997 respectvely. He s an assocate professor at the Department of Measurement and Informaton Systems, Budapest Unversty of Technology and Economcs. Hs current research topcs nclude ntellgent sgnal analyss and processng, medcal mage processng, and decson support systems. 75