EMBEDDED SYSTEM CHIP DESIGN FOR MEDICAL IMAGE INDEXING AND RECOGNITION. Long Xie*, Youichi Kigawa*, Masatoshi Ogawa* Harutoshi Ogai*, Hirokazu Abe#

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1 EBEDDED SYSE CHIP DESIGN FOR EDICAL IAGE INDEXING AND RECOGNIION Long Xe* Youch Kgawa* asatosh Ogawa* Harutosh Oga* Hrokazu Abe# *Department of nformaton producton and system Waseda Unversty Japan #Kushu easurement & Control Inc. akane.waseda.jp Abstract: An embedded system chp s developed for medcal mage ndexng and recognzng through huge medcal mage database. Feature ponts whch can represent the mage are extracted usng ICA algorthm after the mage s preprocessed and dfferent recognton methods are compared to get the optmzed one. he entre project of hardware and software co-desgn s mplemented on Xlnx FPFA wth the consderaton of hgh effcency low power consumng and easy ntegraton wth other devces. Copyrght 00 IFAC Keyword: edcal Image Recognton Independent Component Analyss SoC 1. INRODUCION Wth the development of computer and electronc technology more and more medcal mages from hosptals medcal unverstes and lbrares are stored and processed n dgtal format whch have dfferent characterstc (Vttoro and Lawarence 00) to common mages. he embedded system chp s developed wth the thnkng of system on chp (SoC) to help doctors and researches to get the desrable mages as soon as possble from mage database and to make medcal expert system get the dagnoss and treat results effcently and correctly. Also t s necessary that the chp s low consumng and can cooperate wth other parts of medcal equpment easly. he structure of ths paper s show as below. In Secton the ndependent component analyss (ICA) algorthm based on the measurement of nogentropy s brefly ntroduced and ths algorthm s used to extract feature ponts from the orgnal mages. Followng n Secton 3 all the algorthms are programmed on atlab and C++ platforms the dfference between recognton methods are compared accordng to the extracted feature ponts also the preprocessng and dvson ways of mages are tested to mprove successful recognton rate. he method of developng the hardware platform and the procedure of hardware and software co-desgn on Xlnx FPGA to get the fnal chp s ntroduced n Secton. Furthermore the example data s tested to verfy stablty and effcency of the entre system cooperatng wth mage database.. FIX-POIN ALGORIH OF INDEPENDEN COPONEN ANALYSIS ICA (Comon 1 Lee et al. 000) s a statstcal and computatonal technque for revealng hdden factors that underle n sets of random varables measurements or sgnals. Assume that n mxed sgnals are denoted by column vector X whose

2 elements are demoted by x x... x and the ndependent components of column vector are defned as S wth elements s s... s. Also defne A wth elements a j as mxng matrx. he above model s wrtten as: X = AS (1) ICA s orgnally proposed to solve the blnd source separaton problem to recover n orgnal source sgnals S from the lnearly mxed sgnal X whle assumng as lttle as possble about the natures of 1 mxng matrx A. By defnng the matrx W = A convert the model accordng to (1) as: S = WX () So that the ndependent components can be calculated from the lnear transformaton by W. PCA (prncpal component analyss) (Ruymagaart and Robust 181 Xu and Yulle 1) s a preprocessng step to ICA whch reduces the redundancy of mxed sgnals on a certan level. In the PCA transformaton the vector X s frst centered by subtractng ts mean then E { X } = 0. Next fnd a rotated orthogonal coordnate system such that the elements of X n the new coordnates become uncorrelated and X s lnearly transformed to another vector Y wth m elements m < n so that the redundancy nduced by the correlaton s removed. In the PCA by varance maxmzaton defne that C = E{ XX } (3) x s the n n covarance matrx of X and e e... e are the unt-length egenvectors of the matrx. he egenvectors are ordered accordng to the orderng egenvalues d d... d satsfyng 1 n d > d... d. hen m dmensonal vector Y s: Y = EX () got by choosng the frst m egenvectors of e ( = 1... m) as the row vector of E refer (ppng and Bshop 1) for detals. After reduce the redundancy of mxed sgnals the ICA problem can be greatly smplfed f the sgnals are frst whtened (Cardoso and Laheld ) wth lnear transformaton of Z = VY () 1 / Defne V = D E where E = ( e e... e ) 1 m whose columns are the m egenvectors of C whch has y the same mean as n (3) and D = dag ( d d... d ) 1 m s the dagonal matrx of the egenvalues of C y. Recallng that C y can be wrtten as C = EDE y wth E an orthogonal matrx satsfyng E E = EE = I t holds: 1/ E{ ZZ } = VE{ XX } V = D E EDE ED 1/ = I (6) then t s demonstrated that Y s whtened by V. he fxed-pont algorthm (Hyvärnen 1 Hyvärnen and Oja 000) s realzed usng C++ and atlab to fnd the ndependent components of whtened vector Z. Suppose a transformaton as () s defned as: where S { s s... } S = WZ (7) = 1 s m s the vector of ndependent component. Defne the mutual nformaton I ( s s... s ) = J( S) J( S m ) 1 (8) of s whch s a measurement of the ndependence of random varables J (S) s negentropy gven by J( S) = H( S ) H( S) gauss S s a Caussan random gauss varable of the same covarance matrx as S and H s the dfferental entropy (Papouls 11). o get the desrable matrx W from maxmzng the mutual nformaton (8) of ndependent component s maxmze J ( s ) v = [ E{ G ( W Z )} E{ G ( )}] () under the constran of E{ G( W Z) = 1} wth approprate functon G. he fnal result of W s got by Newton teraton of W + = W μ[ E{ Zg( W Z)} βw]/[ E{ g ( W Z)} β] + + W = W / W (10) where β can be approxmated as E{ Z Zg( W Z)} and μ s a step sze parameter that make the soluton converge faster and g s dervatve functon of G defned n () whch decde the converge speed of ICA algorthm (Hyvärnen Hoyer and Ink 001). For several unts of mxed sgnals the out put matrx W = ( W Z W Z... W Z ) must be decorrelated 1 t after every teraton to prevent the unts from convergng to the same maxma. Follow the teratve algorthm: W = W W C W 1 W = W W C W W (11) z 3 hen the matrx accordng to(10) s symmetrcally decorrelated see (Attas 1). 3. IAGE RECOGNIION AND COPUER SIULAION OF ALGORIH Prmary part functons of hardware system are frst smulated on PC before mplement to FPGA to test z

3 able 1 Comparson of the two recognton methods Calculaton of smlarty dvson Rate of recognton(%) mnmum mean-square error method Fg.1. Feature ponts extracted from mages Proposed recognton method the precson and relatve executon tme whch are mportant for embedded system. Accordng to approprate precson and tme consumng one mage of format JPG s dvded nto 1... n wth nteger n parttons and dgtze each partton as a column of mxed matrx X. he feature ponts of mage are extracted usng ICA algorthm ntroduced above. hen they are compared wth the feature ponts of mages stored n the database whch are also dvded nto same parttons and processed by ICA algorthm. Parts of the feature ponts extracted from some mages are shown n Fg.1. A proposed smlarty calculaton method s developed to recognze the desrable mage from the mage database accordng to the character of ths project and t s compared wth the common mnmum mean-square error method (Jan et al. 000) the sketch map s shown n Fg.. Step1: Get the value of ndependent n and set the approprate number of feature ponts of orgnal key mages whch s compromse of precson and executon tme. Step: Record the poston and sgn of feature ponts of the orgnal mage from the sde of maxmum absolute value of these ponts SEP 1 he feature parameter of a key mage SEP SEP SEP SEP Requred parameter place mrk SEP Factor of key mage A:he feature parameter of the mage to classfy SEP6 SEP6 SEP sort(abs(a)) Fg.. Explanaton of smlarty calculaton of mage Step3: Fnd the most smlar pont from the feature ponts of an mage n database whch means that dfference of the absolute value of the two ponts s the smallest. Step: Calculate the smlarty accordng to smlarty 1 = d m = 0 μx n m (1) x s feature pont poston of the mage n database μ =1 f the sgns of two ponts are same and μ = 0 f opposte. Step: Process the next feature pont of orgnal mage and calculate the smlarty rate f s not satsfed. Step6: If the mage s dvded to number D the smlarty s accumulated followng the results of (1) as: Smlart y = D d = 1 smlarty d (13) he calculaton tme s reduced and precson s mproved usng the method above comparng wth mnmum mean-square error method usually an mage s dvded nto parts accordng to the requrement of real-tme processng. able 1 shows the comparng result of successfully recognzng the desrable mage from mage database between the two methods and the two operatons are down on the same software condtons. Also the nfluence of dvson number of an mage to the successful recognton rate s tested and recommended n the software envronment before mplement to hardware system. A set of calculaton effcency s also tested accordng to the dvson number. Fg.3 shows the relatonshp of dfferent dvson number feature ponts and recognton rate. hs result s the average value for sets of medcal mages of dfferent knd processed on same software envronment. Before extractng the feature ponts usng ICA method the mage can be preprocessed to

4 able Result of recognton rate wth preprocessng dvson Feature ponts 6 Rate of recognton(%) Fg.3. Relatonshp of dfferent dvson number feature ponts and recognton rate get the most necessary secton and remove nose. he dfference before and after preprocessng of effectve feature ponts s shown as Fg. where the mage s processed from vertcal and horzontal drectons to get more precse feature ponts. he number of effectve feature ponts s ncreased and the recognton rate s also mproved. able gves the results of recognton rate wth preprocessng usng proposed recognton method under dfferent dvson number. Remove unnecessary parts and nose. BUILD HE HARDWARE AND SOFWARE ON FPGA Verfyng the desgn dea on FPGA s a good way before the fnal producton of ASIC or FPGA can be used drectly when ts prce s acceptable. he entre system functons are mplemented on emec desgn development board Vrtex P7-67 wth P0 communcatons module. hs board uses Xlnx FPGA supportng IB PowerPC (Xlnx Inc. 003a) also the communcatons module provdes data nterface to exchange data wth outsde. After smulate the man functons of software system on the atlab platform C++ source code of ths project s developed and compled usng GNU comple to verfy the stablty and test the relatve tme consumng on PC also PowerPC supports C++ language and GNU gcc compler so that the C++ source code can be run on PowerPC drectly. Xlnx company provde a seral of development tools whch are effcent to buld the hardware and software systems. he frame of hardware and software co-desgn s as Fg. whch shows the development of hardware platform of IP cores and software platform of C++ source code wth Xlnx EDK tools (Xlnx Inc. 003b). he hardware system s developed usng a seral of IP cores and basng on bus structure. A PowerPC s connected to two K PLB memores over the Processor Local Bus whch are used to store the data Fg. Comparng the feature ponts dfference before and after preprocessng HS Fle HW Spec Ed. EmacsWordPatetc HS Fle HW Plat. Gen EDIFNGC SGP-EngnePlatge VHDB X P.c.h Fles SW Source Ed. EmacsXPSedtetc..c.h lbc.albxl.a S.elf Fle.c.h.elf Fle SW Complers PPC-gcc SW Debuggers PPC-gdb XD Fg.. Hardware and software co-desgn platform

5 algorthms n the feld of pattern recognton wll be tred and the successful recognton rate may be mproved. Furthermore the parts of outsde nterface crcut should be ntegrated nto FPGA possbly to make the entre system more powerful and effcent. REFERENCE Fg.6. Hardware structure on FPGA and nstructons that used by CPU frequently because of hgher processng speed. A UARLte and an Ethernet 10/100 AC are connected to On-chp Perpheral Bus(OPB) provdng the nterface of data transfer between ths chp and outsde devce such as other parts of equpment and mage database. A 3 OPB memory s connected to the OPB bus storng the remanng data and nstructons of compled source code. hs system also contans an nterrupt controller to expand the number of nterrupt nputs to PowerPC and request servce such as data transfer and error processng. A tmer serves to coordnate and handle the event of tme exprng from UARLte and AC to complete the requred system functons. A PLB to OPB brdge s used to connect the two dfferent bus nterface. he hardware structure s shown as Fg.6 refer to (Xlnx Inc. 003c) for detals. he hardware of IP cores s syntheszed and verfed by EDA tool and also the source code and the lbrares for memory management and nterrupt control are compled and lnked to construct the software system. Fnally the hardware part and software part are downloaded to FPGA wth download.bt fle.. CONCLUSION Algorthm analyss software smulaton and hardware mplement are ntroduced n ths paper. ICA algorthm s used to extract the feature ponts of mages makng the mage recognton possble and effcent he algorthm s tested and verfy on PC wth dfferent recognton methods and mage dvson ways. he embedded system FPGA s developed and tested wth some example data. hs desgn also can be used n the felds of securty and fault detecton system wth some modfcaton. Next step s to mprove the effcency of ICA algorthm and make t run faster; also other recognton Attas H. (1). Independent factor analyss. Neural Computaton vol. 11 pp Cardoso J. F. and Laheld B. H. (). Equvarant adaptve source separaton. IEEE ransactons on Sgnal Processng vol. pp Comon P. (1). Independent component analyss a new concept?. Sgnal Process vol. 36 pp Hyvärnen A. (1). Fast and Robust Fxed-Pont Algorthms for Independent Component Analyss. IEEE transacton on neural networks vol. 10 pp Hyvärnen A. and Oja E. (000). Independent Component Analyss: Algorthms and Applcatons. Neural Networks vol.13 pp Hyvärnen A Hoyer P. O. and Ink (001). opographc ndependent component analyss. Neural Computaton vol. 13 pp Jan A. K. Dun R. P. W. and ao J. (000). Statstcal Pattern Recognton: A Revew. IEEE ransactons on Pattern Analyss and achne Intellgence vol. pp. 37. Lee. W. Lewck. S. and Sejnowsk. J. (000). ICA mxture models for unsupervsed classfcaton of non-gaussan sources and automatc context swtchng n blnd sgnal separaton. IEEE ransactons on Pattern Recognton and achne Intellgence vol. pp Papouls A. (11). Probablty Random Varables and Stochastc Processes 3 rd. cgraw-hll USA. Ruymagaart F. and Robust A. (181). Prncpal Component Analyss. J.ultvarate Anal vol. 11 pp Xu L. and Yulle A. (1). Robust prncpal component analyss by self-organzng rules based on statstcal physcs approach. IEEE ransactons on Neural Networks vol. 6 pp ppng. and Bshop C. (1). Probablstc prncpal component analyss. Journal of the Royal Statstcal Socety B Vol.61 pp Vttoro C. and Lawrence D.B. (00) Image Databases chapter. John Wley & Sons Inc.USA. Xlnx Inc. (003a). Embedded System ools Gude. Xlnx USA. Xlnx Inc. (003b). Xlnx Devce Drvers documentaton. Xlnx USA. Xlnx Inc. (003c). PowerPC Processor Reference Gude. Xlnx USA