A High-speed Method for Liver Segmentation on Abdominal CT Image

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1 A High-spd Mthod for Livr Sgmntation on Abdominal CT Imag Wnhan Wang School of Elctronic and Information Enginring Liaoning Tchnical Univrsity Huludao, China Xih Gao Northastrn Univrsity Shnyang, China Abstract This papr prsnts a high-spd livr sgmntation mthod applid on abdominal CT imag. Firstly, basd on th morphological fatur of th livr rgion undr various windowlvl sttings, w apply th rgion-growing algorithm to rmov othr tissus such as sklton, skin, kidny and stomach, and hnc th discrt points of th livr rgion can b acquird. Scondly, w rcovr th livr rgion from th original imag by calculating th coordinats of th discrt points. Finally, in ordr to gt mor accurat sgmntation rsults, th gradint information basd dg corrction and thr-dimnsional rstoration ar adoptd to optimiz th rcovrd livr imag. Compard with othr livr sgmntation mthods, our mthod has lowr tim complxity, which can satisfy th dmands of raltim procssing. Kywords- imag sgmntation; livr; window-lvling tchniqu; thr- dimnsional rstoration sgmntation procss rgarding topological chang [8, 9]. Nvrthlss, th sgmntation mthods suffr from crtain drawbacks. Th snsitivity to th initial contour maks it difficult for th snak modl basd sgmntation mthod to gt satisfying rsults. And for imags with intnsity inhomognity, th livr sgmntation rsults of th gomtric activ contour modls ar far from prfct. Morovr, ths livr sgmntation mthods basd on activ contour modl hav high tim complxity and can hardly satisfy th ral-tim procssing. In this papr, w propos a high-spd sgmntation mthod which maks us of basic imag procssing tchniqus such as th rgion-growing algorithm and combins thm with furthr idas. In contrast to othr livr sgmntation mthods, our mthod can obtain satisfying rsults with lowr tim complxity. I. INTRODUCTION In th last two dcads, modrn imaging tchniqus contributd to th mdical progrss by improving th possibilitis of tchnical imaging. Th improvmnt provids dpr insight into th human body and its bio-mdical procsss, lading to th dvlopmnt of novl scintific intrsts in promoting th diagnosis. Bsids, th modrn imaging tchniqus produc a larg amount of imag matrials which nd to b analyzd by th physicians. Both th nw scintific mthods and th larg amount of imag data rquir th assistanc of th computr with rgard to automatic imag analysis. Thr ar incrasing intrsts for automatic imag analysis in many domains, and for th rason that th accurat sgmntation of livr from th computd tomography (CT) imag can srv as th prrquisit for hpatic disas diagnosis and surgry planning, th automatic livr sgmntation has bcom an important rsarch topic [-5]. Svral mthods ar proposd for th livr sgmntation from CT imags. Paramtric activ contour modls (Snaks) hav bn applid oftn for th sgmntation of th livr rgion [6, 7]. Also, its variational rformation as gomtric activ contour modls can achiv vn bttr prformanc during th livr II. LIVER SEGMENTATION METHOD A. Imag prprocssing Th CT imag has high rsolution and its gray scal is 2 bits (4096 gray lvls). W map th raw imag to th rang of [0, 255] by applying th window- lvling algorithm. According to th prior knowldg of livr, th valu of window width is st at 255 and th valu of window lvl is st at 00. Th transformd imag is shown as Fig. (a). (a) Th transformd imag

2 (b) Th discrt livr imag (c) Th vanscnt livr imag (d) Th discrt points of th livr rgion Figur. Rsults in th prprocssing In th sam way, whn th valu of window width is 2, th CT valu lowr than th widow lvl will b st to 0 whil th valu highr will b st to 255. Th morphological fatur of livr is divrs undr diffrnt sttings of th window lvl. If th window lvl is st at L s, th livr rgion is prsntd as discrt points calld discrt livr imag. In th discrt livr imag, th livr rgion can b distinguishd from othr tissus. Also, if th window lvl is st at L, th livr rgion is totally vanscnt and this imag could b calld vanscnt livr imag. Th discrt livr imag and th vanscnt livr imag ar rprsntd in Fig. (b) and Fig. (c) rspctivly. Furthr analysis indicats that th window lvl stting is positiv proportional to th avrag gray valu of th livr rgion, and th window sttings for discrt livr imag and vanscnt livr imag can b xprssd by th avrag gray valu. Thn, if th avrag gray valu is dnotd by Gman and th radios for discrt livr imag and vanscnt livr imag ar dnotd by k and k2 rspctivly, th valus of L s and L can b dfind as: L L s kg () 2 man k G (2) man In this papr, th statistical mthod has bn usd in ordr to dtrmin th valu of k and k 2. W slct 50 abdominal CT imags as sampls and sgmnt th livr rgion manually so that th avrag gray valu can b obtaind. Thn w calculat th corrsponding ratio k and k 2. According to th xprimntal rsults, th man of k is.8; th standard rror of k is 0.02; th man of k2 is 2.67; th standard rror of k2 is Thrfor, w can st th valu of k at.8 and th valu of k2 at 2.67 within th accptabl rror rang. Nvrthlss, in th obtaind discrt livr imag, othr tissus such as sklton and kidny still xist. Sinc th vanscnt livr imag is involvd with th discrt livr imag, w can us th vanscnt livr imag to rmov th non-livr rgion in th discrt livr imag. During th procss, w adopt th rgion-growing algorithm in which th rgion prsntd in th vanscnt livr imag is slctd as th st of sd points. Thn, th rsults ara of th rgion-growing algorithm is rmovd from th discrt livr imag and th discrt points of th livr rgion shown in Fig. (d) can b achivd. B. Rcovring livr with th discrt points Th prprocssing can bring th discrt points whos contour corrsponds to th actual livr rgion. Thrfor, with th hlp of th discrt points, w ar abl to rcovr th livr rgion from th transformd imag. Th imag quality of th rcovrd livr is rlatd to th dns of th discrt points. Th dnsr th discrt points ar, th mor accurat livr imag w can gt. Th rcovring algorithm is dscribd as follows. First, w calculat th minimum coordinat and th maximum coordinat of th discrt points column by column, and thn rcovr th pixl valus btwn th minimum and th maximum from th transformd imag. For th sak of avoiding th phnomnon of ovr-sgmntation, th diffrnc btwn any two adjacnt coordinats should b lss than th thrshold dnotd by d ( d is st at 50 whn th imag siz is ). Scond, in th sam way, w calculat th minimum coordinat and th maximum coordinat row by row and rcovr th corrsponding pixl valus from th transformd imag. Also, th diffrnc btwn any two adjacnt coordinats should b lss than d. Th rcovrd livr imag is rprsntd in Fig.2 (a)

3 visualization, w rvis th prvious sgmntation rsults in thr dimnsions [0, ]. Fig.3 (a) rprsnts th sagittal livr imag, whil Fig.3 (b) rprsnts th coronal livr imag. Th imags indicat that ovr-sgmntation markd with th rd circl still xist in th obtaind imags. Th rgion-growing algorithm can b usd again to rmov th connctd rgions whos numbr of pixls is lss than n. (a)th rcovrd livr imag. (a) Th sagittal livr imag (b) Th livr imag aftr optimization Figur 2. Sgmntation rsults ) Edg corrction Th rcovrd imag obtaind in th prvious stp has joggd dg and can hardly satisfy th dmands on imag quality. In ordr to solv th problm, w corrct th imag dg by using th gradint information. W rcord th minimum and th maximum of th coordinat row by row. In th nighborhood of th minimum and th maximum, w sarch for th pixl whos normalizd gradint is highr than th thrshold dnotd by g. If th pixl dos xist, w rplac th minimum coordinat or th maximum coordinat with th coordinat of th pixl. Again, th pixl valus btwn th rvisd minimum coordinat and th rvisd maximum coordinat ar rcovrd from th transformd imag. 2) Rmoving th ovr-sgmntation In this stp, w apply th rgion-growing algorithm to calculat th numbr of pixls in ach connctd rgion. If thr ar mor than on connctd rgions in th imag, th connctd rgion whos numbr of pixls is lss than th thrshold dnotd by n will b rmovd from th rcovrd imag, lading to th limination of th ovr-sgmntation. Th optimizd livr imag has bn shown in Fig.2 (b). 3) Thr-dimnsional rstoration Th optimizd livr imag dmonstrats that w can alrady gt satisfying sgmntation rsults. Howvr, th complxity of th livr and th livr disas mak it difficult to sgmnt th livr rgion only basd on th two-dimnsional cross sction. In ordr to achiv bttr prformanc in 3D (b) Th coronal livr imag Figur 3. Rsults in thr-dimnsional rstoration. III. EXPERIMENTAL RESULTS In ordr to xplain th advantags of our mthod, w apply th mthod to mdical imags. Exprimntal data coms from abdominal CT imags which ar collctd from a larg hospital's 64-slic CT machin (spacing btwn layr: 2.0 mm, spatial rsolution: 52 52, imag format: DICOM). Th algorithm is implmntd in C++ and xprimnt nvironmnt is Windows XP, Pntium 4 CPU 3.0 GHz, 52 M RAM. According to th prior knowldg of livr, w slct a spcific ara in thr slics, and calculat th avrag gray valu which can b usd to stand for th avrag gray valu of th whol group. g is st at 0.5, and n is st at 50. Th livr sgmntation rsults ar shown in Fig.4. W compar our mthod with th lvl st modl basd sgmntation mthod and th GVF snak modl basd sgmntation mthod. As shown in th Tabl, our mthod is about 59 and 07 tims mor fficint than th GVF snak modl basd sgmntation mthod and th lvl st modl basd mthod. W can also draw th conclusion that our mthod can satisfy th ral-tim diagnosis in trms of computational fficincy

4 (a) Th original imag. () Th original imag. (b) Th livr rgion obtaind through our mthod (f) Th livr rgion obtaind through our mthod Figur 4. Sgmntation rsults of our proposd mthod. TABLE I. COMPARISON OF OUR METHOD, LEVEL SET MODEL BASED METHOD AND GVF SNAKE MODEL BASED METHOD IN CPU TIME Mthod CPU tim(s) GVF snak modl Lvl st modl 9.75 Our mthod.8 (c) Th original imag. Nvrthlss, whn compard with th sgmntation rsults of th othr two mthods, our mthod has som limitations. For instanc, thr is still ovr-sgmntation phnomnon in th livr imag. Th comparison of sgmntation rsults hav bn shown in Fig. 5. (d) Th livr rgion obtaind through our mthod (a) Th original imag

5 REFERENCES (b) Proposd mthod (c) Lvl st modl basd mthod [] L. Gao, D. Hath, B. Kuszyk, and E. Fishman, Automatic livr sgmntation tchniqu for thr-dimnsional visualization of CT data, Radiology, 996, 20(2), pp [2] L. Solr, H. Dlingtt, G. Malandain, J. Montagnat, Ayach N, and Kohl C, Fully automatic anatomical, pathological, and functional sgmntation from CT scans for hpatic surgry, San Digo, USA: SPIE 2000, pp [3] R. Bichl, C. Baur, A. Bornik, E. Sorantin, and H. Bischof, Livr sgmntation in CT data: a sgmntation rfinmnt approach, Brisban (Australia): Springr-Vrlag, 2007, pp [4] H. Ling, SK. Zhou, Y. Zhng, B. Gorgscu, M. Suhling, and D. Comaniciu, Hirarchical, larning-basd automatic livr sgmntation, IEEE Conf.on Computr Vision and Pattrn Rcognition (CVPR 2008), [5] M. Friman, O. Eliassaf, Y. Taib, L. Joskowicz, Y. Azraq, and J. Sosna, An itrativ Baysian approach for narly automatic livr sgmntation algorithm and validation, Intrnational Journal of Computr Assistd Radiology and Surgry, 3(5), 2008, pp [6] F. Liu, B. S. Zhao, P. Kijwski, M. Ginsbrg, L. A. Wang, and L. Schwartz, Automatic livr contour sgmntation using GVF snak, Mdical Imaging 2004: Imag Procssing, 2004, pp [7] H. Y. Jiang, and X. H. Gao, Smi-automatic livr sgmntation using improvd GVF Snak modl, Advancd Matrials Rsarch, 200, pp [8] J. L, N. Kim, H. L, J. B. So, H. J. Won, Y. M. Shin, Y. G. Shin, and S. H. Kim, Efficint livr sgmntation using a lvl-st mthod with optimal dtction of th initial livr boundary from lvl-st spd imags, Computr Mthods and Programs in Biomdicin, 2007, pp [9] H.Y. Jiang, R. J. Fng, and X. H. Gao, Lvl st basd on signd prssur forc function and its application in livr sgmntation, Wuhan Univrsity Journal of Natural Scinc, 200, pp [0] S. P. Liou, and R. C. Jain, An Approach to Thr Dimnsional Imag sgmntation, Computr Vision, Graphics and Imag Procssing, 99, pp [] D. Y. Suh, R. L. Eisnr, R. M. Mrsrau, and R. I. Pttigrw, Knowldg-basd systm for boundary dtction of four-dimnsional cardiac magntic rsonanc imag squnc, IEEE Trasactions on Mdical Imaging, 2(), 993, pp (d) GVF snak modl basd mthod Figur 5. Comparison of sgmntation rsults IV. CONCLUSIONS W propos a high-spd mthod for livr sgmntation in this papr, and th application of th mthod provd ffctiv in th automatic livr sgmntation from abdominal CT imags. In contrast to modl-basd sgmntation mthods, such as sgmntation mthod basd on lvl st modl and sgmntation mthod basd on GVF snak modl, our mthod is suprior in fficincy and has mor practical valu. Howvr, th sgmntation rsults of th proposd mthod hav som problms lik ovr-sgmntation. Thrfor, how to gt mor accurat sgmntation rsults will b focusd on in our futur rsarch