Image Segmentation with PCNN Model and Immune Algorithm

Transcription

1 JOURNAL OF COMPUTERS, VOL. 8, NO. 9, SEPTEMBER Image Segmentaton wth PCNN Model and Immune Algorthm Janfeng L School of Informaton scence and Engneer, Central South Unversty, Changsha , Chna) School of Informaton Scence and Engneerng, Jshou Unversty, Jshou , Chna Emal: ljf_zy@163.com Be Zou School of Informaton scence and Engneer, Central South Unversty, Changsha , Chna) Emal: bjzou@163.com Le Dng School of Informaton Scence and Engneerng, Jshou Unversty, Jshou , Chna E-mal: dngle_39@yahoo.com.cn Xu Gao School of Informaton scence and Engneer, Central South Unversty, Changsha , Chna) Emal: @qq.com Abstract In the doman of mage processng, PCNN (Pulse Coupled Neural Network) need to adjust parameters tme after tme to obtan the better performance. To ths end, we propose a novel PCNN parameters automatc decson algorthm based on mmune algorthm. The proposed method transforms PCNN parameters settng problem nto parameters optmzaton based on mmune algorthm. It takes mage entropy as the evaluaton bass of the best ftness of mmune algorthm so that PCNN parameters can be adjusted adaptvely. Meanwhle, n order to break the condton that populaton nformaton fall nto local optmum, the proposed method ntroduces gradent nformaton to affect the evoluton of antbody to keep the populaton actvty. Experment results show that the proposed method realzes the adaptve adjustment of PCNN parameters and yelds the better segmentaton performance than many exstng methods. Index Terms pulse couple neural network; Immune Algorthm; Image Segmentaton; Parameter optmzaton; Image Entropy; Ftness functon I. INTRODUCTION PCNN (Pulse Coupled Neural Network) s bonc mathematcal model proposed by Eckhorn based on Synchronous pulse release phenomenon of the cat's vsual cortex neurons. Because the work mechansm of PCNN s smlar to the actvtes of human vsual cortex neurons, the model s wdely used n mage processng[1],mage fuson[2], mage denosng [3],target recognton[4],mage compresson[5].at present, ths algorthm has two promnent problems:one s that n theory t s dffcult to explan the exact relatonshp between the varous parameters n PCNN mathematcal model and the mage processng performance so that the mage processng performance s good or not depends on the contnuous adjustment of parameters and the selecton of many experments; the other s that the model has lots of parameters and a complex calculaton. Due to the above reason, many researchers devote to PCNN parameters optmzaton and self-adapton, there are three typcal methods. The frst method sets adjustment condton to stop PCNN algorthm through evaluatng mage processng results so that PCNN terates automatcally, e.g., Zhao Shang et al. [6] took the connected doman of the mage segmentaton results as the evaluaton condton, Ma Yde et al. [7] proposed the stop crteron named as 2D-OTSU, Yao Chang et al. [8] used the cross entropy of segmentaton results as PCNN stop crteron, Xn Guojang et al. [9] took the maxmum varance rato of segmentaton results as PCNN stop condton. The second method fnds out the characterstcs and changng trends of the parameters n dfferent mage processng tasks through analyzng the characterstcs (mechansm) of PCNN parameters qualtatvely and quanttatvely, then gven the specfc calculaton formula for each parameter[1],[10],[11]. The thrd method transforms parameter settng nto multobjectve optmzaton problem. Concernng dfferent mage processng tasks, t searches optmal parameter combnaton by means of optmzaton algorthm to acheve PCNN self-adapton, e.g., Ma Yde etal. [12] use the genetc algorthm to optmze PCNN parameters, Xu Xnzheng et al. [13] use partcle swarm optmzaton algorthm to optmze PCNN parameters, BI Xaojun et al.[14] use mmune algorthm to optmze PCNN parameters. Although BI Xao-jun et al. [14] successfully resolved the PCNN parameters self-adapton problems through the mmune clone algorthm, as a ntellgent algorthm, mmune clone algorthm nevtably have to face some do: /jcp

2 2430 JOURNAL OF COMPUTERS, VOL. 8, NO. 9, SEPTEMBER 2013 problems such as precocty and stagnaton. Based on the research work of predecessors we put forward a new dea that optmzes PCNN parameters combned wth mproved mmune algorthm. The proposed method transforms PCNN parameters settng problem nto parameters optmzaton based on mmune algorthm by usng the characterstc that mmune algorthm has global optmzaton and fast convergence. In order to break the condton that group nformaton fall nto local optmum, the proposed method ntroduces gradent to affect the evoluton of antbody to keep the group actvty and takes Shannon entropy as the bass of evaluatng mmune algorthm s good or not. Fnally, ths method s appled to the specfc mage segmentaton tasks. Experment results show that ntroducng gradent nformaton enhances the mmune algorthm executon speed and PCNN can acheve parameters automatc optmzaton after optmzed by mmune algorthm, the optmzed parameters are able to yeld good mage segmentaton performance. II. RELATED WORKS A. PCNN Model PCNN s two-dmensonal network whch s composed by M N PCNs(pulsed coupled neural). The model has two characterstcs: well mtatng the fatgue and refractory perod of vsual cortex nerve cells; beng good at classfyng the pxels whch ther gray value s smlar to each other. A sngle neuron s composed by three parts: the recevng doman, the modulaton part and the pulse generator. The correspondng dscrete mathematcs equatons are descrbed below [15]: a F ( ) e F n = F ( n 1) + VF MklYkl ( n 1) + S (1) kl a L ( ) e L n = L ( n 1) + VL WklYkl ( n 1) (2) U ( n) = F ( n)(1 + β L ( n)) (3) 1, U ( n)>e Y ( n) = 0, other kl ( n 1) a E E (4) E ( n) = e E ( n 1) + V Y ( n) (5) In the formula(1)-(5), F,L,U,E,Y,S respectvely stands for feedback nput, lnk nput, nternal actvty, dynamc threshold, pulse output, external stmulaton; and j are neuron labels; n s teratons; W and M are weght matrxes; W kjl and M kl are neuron synaptc connecton weghts(generally, W kjl =M kl ); a L,a F,a E and V L,V F,V E respectvely stand for tme attenuaton constants and ampltude coeffcents;β s nternal actvty connecton coeffcent. In mage processng tasks, one pxel corresponds to one PCN, they consttute a two-dmensonal network. Frst, external stmulaton S(gray value of pxel n mage processng) s nput to recevng doman and fused wth the output Y of the adjacent neurons, then the feedback nput F s obtaned, lnk nput L receves Y, nternal actvty multples F by L to get U, n pulse generator part, U s compared wth dynamc threshold E, f U>E, the pulse generator starts neuron gnton, outputs Y = 1, else neuron msfres, outputs Y = 0. The above process s repeated, untl t satsfes the condtons. Due to lots of PCNN parameters, t s dffcult to determne parameters artfcally, and the calculaton s large, the effcency s low [15]. Ths paper uses the smplfed PCNN model, the correspondng dscrete mathematcs equatons are descrbed below [16]: F ( n) = S (6) L ( n) = VL WklYkl ( n 1) (7) kl U ( n) = F ( n)(1 + β L ( n)) (8) 1, U ( n)>e Y ( n) = 0, other ( n 1) a E E (9) E ( n) = e E ( n 1) + V Y ( n) (10) In the formula (6)-(10), the meanng of the parameters s as same as the above formula (1)-(5). Compared wth orgnal model, the mproved model cuts down the number of the parameters from 9 to 5 and reduces the complexty of the parameters settng. B. Immune Clone Selecton Algorthm:ICSA In 1999, based on bologcal mmune clone selecton prncple, Dr. De Castro proposed the new ntellgent algorthm: Immune Clone Selecton Algorthm. Ths Algorthm shows excellent performance and effcency n machne learnng, anomaly and fault dagnoss, robot behavour smulaton and control, network ntruson detecton and functon optmzaton, etc. It transforms the problems and ther correspondng solutons nto the antbody and antgen of bologcal mmune system, and uses the accessblty and proporton between antbody and antgen to select and clone antbody, and remembers the excellent antbody by constructng a memory unt, and realzes the dversty of populaton through generatng new antbody to substtute old antbody, fnally acheves the purpose of solvng practcal problems. Ⅲ. OPTIMIZING THE PCNN MODEL COMBINED WITH GRADIENT INFORMATION IMMUNE ALGORITHM Based on mmune algorthm, ths paper combnes the excellent gene nformaton of antbody and the characterstcs of PCNN parameters to optmze PCNN parameters. In the optmzaton algorthm, t performs receptor edtng to antbody gene and ntroduces gradent nformaton to affect the evoluton of partal antbody to keep the populaton actvty. A. Related Defnton Defnton 1: Antgen. Image entropy s a knd of wdespread evaluaton ndex of mage segmentaton results, the bgger entropy value shows that the more nformaton that the segmentaton results obtan from orgnal mage. In ths paper, the mage entropy s defned as the antgen ( max f ( W ) W Ω, Ω s the feasble doman of W) of mmune parameter optmzaton.

3 JOURNAL OF COMPUTERS, VOL. 8, NO. 9, SEPTEMBER Defnton 2: Antbody. Antbody s the optmzaton soluton of mmune algorthm. In ths paper, the optmzed PCNN parameters (ampltude coeffcent V L, nternal connecton coeffcent β, tme decay constant a E and V E ) compose the vector (V L, β, a E, V E ) whch s defned as antbody, then the antbody set correspondng to antbody populaton can be expressed as W = {( VL, β, ae, VE) VL, β, ae, VE [0,1]}. Defnton 3: Memory Antbodes. Memory antbody set s composed by a certan number of hghest-affnty antbodes selected from antbody set and the antbodes n the memory antbody set are defned as memory antbodes. These antbodes drectly enter nto the next generaton of populaton evoluton to ensure that the global convergence of populaton. The sze of memory antbody set should be moderate, n ths paper, t takes 10-20% of the sze of antbody set. Defnton 4: Antbody Densty. Antbody densty means the percentage of the number of antbodes that are concentrated or have larger affnty currently n the antbody set. In ths paper, t takes Eucldan dstance between antbodes as the evaluaton bass of antbody smlarty. So the antbody densty s defned as DENSITY = V / N, where V s the number of the antbodes that have large affnty wth W, N s the total number of antbody. The calculaton process frst adopts formula (11) to acqure the Eucldan dstance between antbodes, and then uses formula (12) to calculate antbody smlarty, fnally, calculates antbody densty by use of formula (13). the correspondng calculaton equatons are descrbed below: 4 j =, k j, k 2 (11) k = 1 DAb (, Ab) 1 ( w w ) 1, D( Ab, Abj) θ s( Ab, Abj) = 0, otherwse DENSITY ( Ab ) N sabab ( ) (12), k = k = 1 (13) N where θ represents the gven threshold of antbody smlarty, and N s the total number of antbodes n the antbody populaton. B. Codng and Intalzaton In ths paper, antbody s encoded by a 11 bt bnary number. Meanwhle, n order to ncrease the dversty of the ntal populaton, the algorthm utlzes random ntalzaton strategy, and then randomly generates M antbodes as ntal populaton. C. Immune Evoluton Immune evoluton s realzed through crossover, mutaton, clonal varaton, selecton, etc. Crossover Operator: accordng to the codng characterstc, ths paper adopts arthmetc crossover operaton as the crossover operator. The algorthm randomly selects two antbodes on the bass of cross probablty P c to execute crossover operaton. It concretely swaps the varables at each dmenson n the two antbodes accordng to the certan probablty. Take the varable at the frst dmenson for example, f the two antbodes before crossover are W 1 (w 11, w 12, w 13, w 14 ) W 2 (w 21, w 22, w 23, w 24 ), and after crossover they change nto W 1 (w 21, w 12, w 13, w 14 ) W 2 (w 11, w 22, w 23, w 24 ). When the average affnty of the antbody set changes lttle or has no change for contnuous generatons, the algorthm agan executes crossover operaton on the bass of probablty P c =(1+a c )P c, where a c =e -fa, f a represents the change of average affnty before and after several generatons. Mutaton Operator:the algorthm randomly selects antbody to execute mutaton operaton on ts bnary bts accordng to the mutaton probablty P b. When the average affnty of the antbody set changes lttle or has no change for contnuous generatons, n order to jump out local optmum and ncrease the degree of mutaton, the algorthm ncrease mutaton probablty for the randomly selected antbodes. It concretely execute mutaton operaton for the bnary bts accordng to the mutaton probablty P b =(1+a c )P b, where a c =e -fa, f a represents the change of average affnty before and after several generatons. Clonal Varaton:clonal varaton smulates the hgh frequency varaton n bologcal mmune process, t ams at ensurng the global convergence, enhancng local searchng ablty and makng the algorthm quckly converge to global optmum. Concretely, t constructs temporary clone antbody set C* through clonng every antbody n the antbody set C whch s acqured by mmune algorthm, and then mutates every antbody n the set C*, where C* = N C C, N C s the number of clone antbody of each parent antbody. In the antbody set C*, t respectvely compares the antbodes after clonal varaton wth ther parent antbodes accordng to affnty, and retans the better antbodes to construct a new antbody set C whch has a same sze wth C. Fnally, t compares C wth the memory antbody set and substtutes the better antbodes n C for antbodes whch has low affnty n the memory antbody set, and then obtans a new memory antbody set. Selecton Operator:accordng to mmune memory mechansm, the selecton operaton n ths paper ncludes two condtons. One s that the antbodes n memory antbody set drectly enter nto the evolutonary populaton of next generaton. The other s that, amng at a constant populaton sze, t selects the remanng antbodes n the antbody set C on the bass of affnty to enter nto the evolutonary populaton of next generaton.

4 2432 JOURNAL OF COMPUTERS, VOL. 8, NO. 9, SEPTEMBER 2013 When the evoluton stops, accordng to the mechansm that mmune system can dynamc randomly generate new antbodes, t randomly generates d 1 antbodes to substtute the antbodes whch have low affnty n the prevous antbody set and randomly generates d 2 antbodes to substtute the antbodes whch have relatvely hgh densty, and then the dversty of the antbodes can be ncreased. Introducng gradent nformaton:for functon f ( x ), where x = ( x1, x2,, x n ), the gradent of f ( x ) s shown n the equaton(14): f f f f f ( W ) = (,,, ) (14) W1 W2 W3 W4 When mmune algorthm stops evolvng or evolves very slowly (the algorthm stops convergng or converges slowly), n order to accelerate the convergence rate and mprove searchng performance, t randomly selects partal antbodes and searches along ther gradent drecton. Concretely, the proposed method randomly selects some antbodes to ntroduce the gradent nformaton n the mutaton process, and acqures acceleraton step through lnear searchng along the negatve gradent drecton to make the algorthm search along the drecton that antbody ftness fastest declne. D. Calculatng Affnty The affnty between Antgen and antbody correspond to the matchng degree between soluton and objectve functon, the hgher affnty means the better antbody. Image entropy reflects the amount of nformaton that the segmented mage acqures from orgnal mage, therefore ths paper takes mage entropy as the evaluaton bass of the affnty of mmune algorthm, and the formula s shown below: L 1 ft = H = p log p (15) = 0 2 Where L s gray level, generally equals 256, 0 L, p s the probablty of gray value appearng n mage. E. Algorthm Steps Step1: The generaton of the ntal antbodes. It randomly generates N ntal antbodes to construct antbody set Ab(Ab Ab, =1,2,,N), and randomly selects some antbodes to construct ntal memory antbody set, n our experments, N = s taken. Step2: Affnty calculaton. It calculates the affnty ft N of antbody and the average affnty ( vmft = ( ft )/ N ) = 1 of the antbody set, where ft represents the affnty of the th antbody. Step3: For antbody set, t executes crossover operaton accordng to Crossover Operator, and executes mutaton operaton accordng to Mutaton Operator. Step4: Clonal Varaton. It executes clonal varaton operaton accordng to Clonal Varaton. The number of the clone antbodes has a great mpact on the convergence speed of the algorthm. When there are more clone antbodes, the evoluton speed s faster, antbodes need fewer generatons to move to the extremal pont, but the tme complexty of the algorthm wll ncrease. In order to balance the convergence speed and the tme complexty, n our experments, clone control coeffcent k=20 s taken. Step5: It executes selecton operaton accordng to Selecton Operator and selects the correspondng antbodes to enter nto the next generaton of populaton evoluton. Step6: If t satsfes the maxmum generatons Ngen of termnaton, the algorthm termnates; else goes to Step 2. IV. EXPERIMENTS AND DISCUSSION A. Usng Test Functon to Verfy the Improved Immune Algorthm We compared the method n ths paper wth the basc mmune algorthm by use of the test functon shown n formula (16). The characterstcs of ths test functon ncludes multmodal, mult-varable and mult-extreme. Through test optmzaton algorthm, we can fnd out the number of maxmum ponts and the number of extreme ponts and the run tme of the test functon to evaluate the global optmzaton ablty, the stablty and tme effcency of the optmzaton algorthm. In the formula, x y [ 1,2]. x sn(4 π x) y sn(4πy+ π + 1) (16) The data n the table 1 shows that both the mproved mmune algorthm and the basc mmune algorthm are able to fnd out the global optmal soluton, they also fnd the same number of maxmum ponts, but our method takes less tme than the mmune algorthm. The above comparson shows that the proposed method mantans the performance of the mmune algorthm after ntroduced gradent nformaton and has excellent global optmzaton ablty and algorthm stablty; meanwhle, t s better than the basc mmune algorthm n run tme. TABLE 1 THE PERFORMANCE COMPARISON BETWEEN STANDARD IMMUNE ALGORITHM AND THE METHOD AFTER INTRODUCED GRADIENT test tmes Can fnd the optmal soluton or not The The basc proposed mmune method algorthm The number of extreme ponts The proposed method The basc mmune algorthm The proposed method Run tme(s) The basc mmune algorthm 1 YES YES YES YES YES YES YES YES YES YES B. Comparson of Image Segmentaton Performance We pcked orgnal mages from varous feld, such as medcne, mnng, Marne lfe and real lfe. These mages have low contrast and ununformly dstrbuted grayscale. Example mages are shown n Fg.1:(1) Lena(2)bacteral mage 1(3)bacteral mage 2(4)chalk mage (5)bubble mage (6)fsh mage. Image segmentaton algorthms used for comparson are PCNN, Maxmum between-cluster varance(otsu), Partcle swarm optmzaton-

5 JOURNAL OF COMPUTERS, VOL. 8, NO. 9, SEPTEMBER PCNN(PSO-PCNN), maxmum entropy, cross entropy, regon unformty, watershed algorthm. Fgure 3. the actual segmentaton results of OTSU, Maxmum entropy, proposed method for bactera 1 mage. Fgure 1. Some orgnal mages used n our experments. We compared performance of algorthms n terms of subjectve vsual preference and objectve evaluaton. Subjectve vsual preference s used to evaluate the vsual preference of the segmentaton. The better the vsual effect s, the more the segmentaton s n accordance wth the actual human vsual preference. Fg.2 to Fg.7 shows the segmentaton result of our algorthm and other algorthms. In terms of subjectve vsual preference, better segmentaton are obtaned n lena mage,bacteral mage2,chalk mage,bubble mage and fsh mage due to no subjectve goal was set. From the segmentaton of these mages, we can see more layers, complete regon, obvous detal features were captured. As for the bacteral mage 1, dfferent segmentaton produced by our algorthm and other algorthms. Our algorthm, PSO- PCNN PCNN and cross entropy can dstngush the foreground and background properly, but the rest algorthms suffered from over-segmentaton. Fgure 4. the actual segmentaton results of OTSU, Maxmum entropy, proposed method for bactera 2 mage OTSU Maxmum Entropy Cross-entropy Watershed algorthm Regonal consstency algorthm PCNN PSO-PCNN Our method Fgure 5. the actual segmentaton results of OTSU, Maxmum entropy, proposed method for chalks mage Fgure 2. the actual segmentaton results of OTSU, Maxmum entropy, proposed method for Lena mage. Fgure 6. the actual segmentaton results of OTSU, Maxmum entropy, proposed method for bubbles mage.

6 2434 JOURNAL OF COMPUTERS, VOL. 8, NO. 9, SEPTEMBER 2013 Fgure 7. the actual segmentaton results of OTSU, Maxmum entropy, proposed method for fsh mage. As for objectve evaluaton, we used ndces such as regon unformty(u), regon shape(s),regon contrast(c) and entropy(e) to compare the performance of our algorthm and other algorthms. The more the value obtaned from the above ndces s approached to 1, the better the segmentaton are. Fgure 8 to fgure 11 are the comparson result. Snce dfferent segmentaton crtera reflect only certan aspect of the performance of algorthms. We use Eq.(17)to evaluate the compound performance of algorthms. The performance of algorthms compared n fgure 12. From fgure 8 to table 12, we can see that our algorthm excels at both the ndvdual ndex comparson and the compound evaluaton. (17) Z =U S C E Fgure 9. the value of regon contrast by OTSU, Maxmum entropy, Cross entropy, Watershed, Regon consstency, PCNN, PSO-PCNN and the proposed method for test mage. Fgure 10. the value of regonal shape by OTSU, Maxmum entropy, Cross entropy, Watershed, Regon consstency, PCNN, PSO-PCNN and the proposed method for test mage. Fgure 8. the value of regon homogenety by OTSU, Maxmum entropy, Cross entropy, Watershed, Regon consstency, PCNN, PSOPCNN and the proposed method for test mage. Fgure 11. value of entropy by OTSU, Maxmum entropy, Cross entropy, Watershed, Regon consstency, PCNN, PSO-PCNN and the proposed method for test mage.

7 JOURNAL OF COMPUTERS, VOL. 8, NO. 9, SEPTEMBER Fgure 12. value of equaton[17] by OTSU, Maxmum entropy, Cross entropy, Watershed, Regon consstency, PCNN, PSO-PCNN and the proposed method for test mage. C. Medcal Image Segmentaton We took a further comparson between the proposed method and other methods through medcal mage segmentaton whch has explct segmentaton purpose and practcal sgnfcance. Medcal mage segmentaton ams at extractng the regons whch have practcal sgnfcance from organs and organzatons, but t s dffcult to obtan a deal segmentaton results because of the nose, the texture of organs and organzatons, etc. The above stuaton results n that medcal mage segmentaton s stll a worldwde problem so far, and therefore t has certan sgnfcance to evaluate the performance of segmentaton algorthms through medcal mage segmentaton. The frst mage n Fgure 13 s a nuclear magnetc resonance magng (NMRI) wthout any preprocessng for typcal cerebral hemorrhage, where redlne marked regon s the actual poston of bleedng. Although doctor can make a defnte dagnoss of cerebral hemorrhage, t s necessary to segment ths regon precsely to calculate the amount of bleedng and the sze of bleedng regon. Fgure 13 shows the segmentaton results of the proposed method and other algorthms. Fgure 13. The comparson between the proposed method and other algorthms by bran mage segmentaton results. Fgure 13 shows that the proposed method, PSO- PCNN, PCNN and Maxmum entropy can manly extract the accurate bleedng regon, but the segmented regon of the proposed method satsfes the actual demand better, meanwhle PSO-PCNN, PCNN and Maxmum entropy exst more serous over-segmentaton phenomenon so that some organzatons rrelevant to leson regon also are extracted. Although Watershed, OTSU, Cross entropy and Regon consstency can obtan the better vsual segmentaton results, the results can not reflect the bleedng regon precsely. D. Analyss of Expermental Results (1) Analyss of algorthm ntegrated performance. Accordng to the subjectve results of fgure 2 and fgure 3 and the objectve evaluaton of fgure 4, the proposed method as a whole has a better performance among the algorthms. Besdes, accordng to the medcal mage segmentaton results of fgure 5 and fgure 6, the proposed method s superor to other algorthms, PCNN combned wth human vsual characterstcs can obtan the better results n mage segmentaton tasks to satsfy the subjectve percepton of human vson. (2) Comparson among the same-type algorthms. In dfferent segmentaton tasks, the results of the proposed method s superor to PCNN and PSO-PCNN n both subjectve vson and objectve evaluaton. Ths s because the parameters of the PCNN completely dependent on artfcal regulaton n the mage segmentaton tasks, there s no rules to follow for artfcal settng pattern of parameters, and the mechansm of varous parameters and the law between each other stll do not get a good proof, the good segmentaton results completely rely on experence and a lot of experments, therefore, the optmal parameter combnaton can not be found n the fnte experments. PSO-PCNN optmzes PCNN parameters dependng on PSO whle the proposed method optmzes PCNN parameters through combnng wth mmune algorthm. LI Lny et al. [17] ndcate that mmune algorthm compared wth PSO has the hgher global optmzaton ablty and the ablty of jumpng out of local optmal soluton. Therefore, the proposed method combned wth mmune algorthm can obtan the more reasonable parameters than PSO-PCNN. V. CONCLUSIONS Ths paper uses the gradent nformaton based mmune algorthm to optmze PCNN parameters and apples ths method to mage segmentaton. The algorthm combnes wth the automatc optmzaton characterstc of mmune algorthm and fuse PCNN parameters nto the optmzaton tasks of mmune algorthm, so that t better solves the problem that PCNN parameters need artfcal adjustng. Experments verfy that mmune algorthm can be used to optmze PCNN parameters and t can obtan a better mage segmentaton performance wth ths optmzed parameters.

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