Comparison for Edge Detection of Colony Images

Transcription

1 IJCSNS Inernaional Journal of Compuer Science and Nework Securiy, VOL.6 No.9A, Sepemer Comparison for Edge Deecion of Colony Images Wang Luo Universiy of Elecronic Science and Technology of China, Chengdu, China and Jinggangshan Universiy, Jiangxi, China Summary The exracion of feaures such as edges and curves from an image is useful for many purposes. Feaures, such as edges and curves are useful in i) exure analysis ii) 3-D surface resrucuring iii) segmenaion iv) image maching. Edges are imporan feaures in an image since hey represen significan local inensiy changes. They provide imporan clues o separae regions wihin an ojec or o idenify changes in illuminaion. Mos remoe sensing applicaions use edge deecion as a preprocessing sage for feaure exracion. Real images, such as remoe sensing images, can e corruped wih poin noise. The real prolem is how o enhance noisy remoe sensing images and simulaneously exrac he edges. Using he implemened Canny edge deecor for feaures exracion and as an enhancemen ool for remoe sensing images, he resul was rous wih a very high enhancemen level. Oherwise, he idea of using oh inensiies and spaial informaion has een considered o ake ino accoun local informaion used in human percepion. The approach proposed here is srongly relaed o his idea. The search sraegy is ased on a Geneic Algorihm (GA) ha allows us o find suiale approximaed soluions reaing he prolem as a gloal opimizaion echnique. Key words: Canny edge deecor, Edge deecion, geneic algorihm 1. Inroducion A large numer of edge deecion echniques have een proposed. The common approach is o apply he firs (or second) derivaive o he smoohed image and hen find he local maxima (or zero-crossings). An imporan issue in edge deecion is he scale of deecion filer. Small-scaled filers are sensiive o edge signals u also prone o noise, whereas large-scaled filers are rous o noise u could filer ou fine deails. Muliple scales could e employed o descrie and synhesize he varieies of edge srucures. Malla [1] illusraed mahemaically ha signals and noise have differen singulariies and edge srucures presen oservale magniudes along he scales, while noise decreases rapidly. Wih his oservaion, Xu e al. [2] proposed a wavele-ased spaially selecive filering echnique y muliplying he adjacen scales. Sadler and Swami [3] applied he wavele-muliscale-producs o sep deecion and esimaion and Bao and Zhang [4] presened a denoising scheme y hresholding he muliscale producs. Canny [5] firs presened he well-known hree crieria of edge deecors: good deecion, good localizaion, and low spurious response and showed ha he opimal deecor for an isolaed sep edge should e he firs derivaive of Gaussian. Edge deecion of images does no admi a unique soluion ecause sujeciveness and cones may ake par in he decision phase. I follows ha general soluions are no possile and each proposed echnique can only e used o solve a class of prolems. The approach proposed in [6] ased on Geneic algorihms considers he prolem of exracing he larges image regions ha saisfy uniformiy es in he inensiy-spaial domain. 2. Tradiional edge deecors An edge defined in an image as a oundary or conour a which a significan change occurs in some physical aspec of he image. Edge deecion is a mehod as significan as hreshold. Four differen edge deecor operaors are examined and i is shown ha he Soel

2 212 IJCSNS Inernaional Journal of Compuer Science and Nework Securiy, VOL.6 No.9A, Sepemer 2006 edge deecor provides very hick and someimes very inaccurae edges, especially when applied o noisy images. The LoG operaor provides slighly eer resuls. Tradiional edge deecors were ased on a raher small 3x3 neighorhood, which only examined each pixel s neares neighor. This may work well u due o he size of he neighorhood ha is eing examined, here are limiaions o he accuracy of he final edge. These local neighorhoods will only deec local disconinuiies, and i is possile ha his may cause false edges o e exraced. A more powerful approach is o use a se of firs or second difference operaors ased on neighorhoods having a range of sizes (e.g. increasing y facors of 2) and comine heir oupus, so ha disconinuiies can e deeced a many differen scales. Edges can e deeced in many ways such as Laplacian Roers, Soel and gradien. In oh inensiy and color, linear operaors can deec edges hrough he use of masks ha represen he ideal edge seps in various direcions. They can also deec lines and curves in much he same way. Gradien operaors, Laplacian operaors, and zero-crossing operaors are usually used for edge deecion. The gradien operaors compue some quaniy relaed o he magniude of he slope of he underlying image gray one inensiy surface of which he oserved image pixel values are noisy discreized samples. The Laplacian operaors compue some quaniy relaed o he Laplacian of he underlying image gray one inensiy surface. The zero-crossing operaors deermine wheher or no he digial Laplacian or he esimaed second direcion derivaive has a zero-crossing wihin he pixel. There are many ways o perform edge deecion. However, he mos may e grouped ino hree caegories, gradien (Approximaions of he firs derivaive), Laplacian (Zero crossing deecors) and Image approximaion algorihms. Edge deecors ased on gradien concep are he Soel, Roers and Prewi Fig. 2 (), 2 (c), 2 (d) show he effec of hese filers on he remoe sensing images. The major drawack of such an operaor in segmenaion is he fac ha deermining he acual locaion of he edge, slope urnovers poin, is difficul. A more effecive operaor is he Laplacian Fig. 2 (d), which uses he second derivaive in deermining he edge. Fig. 1 colony image (a) () (c) (d) Fig. 2 (a) Edge map using Soel operaor () Edge map using Roers operaor (c) Edge map using Prewi operaor (d) Edge map using Laplace operaor The gradien of an image f(x, y) a locaion (x, y) is defined as he vecor f G x x (1) f = = Gy f y and he magniude and direcion of he gradien are:

3 IJCSNS Inernaional Journal of Compuer Science and Nework Securiy, VOL.6 No.9A, Sepemer G G x + G y 2 2 = (2) where he angle is measured wih respec o he x-axis. The direcion of an edge a (x, y) is perpendicular o he direcion of he gradien vecor a ha poin. 3. Canny edge deecion of colony image (a) () The Canny edge deecor [2] is ased on compuing he squared gradien magniude. Local maxima of he gradien magniude ha are aove some hreshold are hen idenified as edges. This hreshold local peak deecion mehod is called non-maximum suppression, or NMS. The moivaion for Canny's edge operaor was o derive an opimal operaor in he sense ha minimizes he proailiy of muliply deecing an edge, minimizes he proailiy of failing o deec an edge and minimizes he disance of he repored edge from he rue edge. The firs wo of hese crieria address he issue of deecion, ha is, given ha an edge is presen will he edge deecor find ha edge (and no oher edges). The hird crierion addresses he issue of localizaion, which is how accuraely he posiion of an edge is repored. There is a radeoff eween deecion and localizaion -- he more accurae he deecor he less accurae he localizaion and vice-versa. The ojecive funcion was designed o achieve he following opimizaion consrains: (i) Maximize he signal o noise raio o give perfec deecion. This favours he marking of rue posiives. (ii) Achieve perfec localizaion o accuraely mark edges. (iii) Minimize he numer of responses o a single edge. This favours he idenificaion of rue negaives, ha is, non-edges are no marked. These crieria seem o e reasonale candidaes for filers comparison. Fig. 3 shows a comparison eween edge maps using differen Canny edge deecor Fig. 3 (a) Edge map using Canny operaor which sigma is 0.6 () Edge map using Canny operaor which sigma is An applicaion of GAs o edge deecion of colony image Deecion of he edge in he colony image wih randomness is very difficul and imporan ask. A mehod proposed in [7] using geneic algorihms formulae he edge deecion prolem as a cominaorial opimizaion prolem and deecion of he edge is execued according o he variance of exure feaure in he local area. Firs, we elec he candidae edge regions and hen apply GAs in order o decide he opimum edge regions. This mehod using GAs has an advanage ha arrangemen of he edge regions is fulfilled y very simple archiecure and i does no need much processing ime. The geneic algorihm used o find he es weighing parameers may e skeched as follows: Program Ga_research Se up a random populaion of chromosomes ( ) = { ( 0), ( 0),..., ( 0) }; 0; P m Iniialize in P ( 0) ; While <G wih he value of he es chromosome for each ( ) P( ) evaluae f ( () ) i find he es chromosome () P() i if f ( ( ) ) f ( ) hen ( )

4 214 IJCSNS Inernaional Journal of Compuer Science and Nework Securiy, VOL.6 No.9A, Sepemer 2006 Apply crossover and muaion o he curren populaion P(), according o crossover proailiy (pc) and muaion proailiy (pm); Apply inary ournamen selecion o he emporary populaion Oained from he previous insrucion; +1; end no usually reached. The edge deecion resul shows in Fig.4. Give an inpu image, I (i, j), of size n m. The geneic chromosome α i, j is coded y a 32 i inary sring ha codes he pixel-lael, λ in he 8 less significan is and he pixel posiion (i, j) in he 24 mos significan is. Here, λ idenifies he clusers o which he pixel elongs. Each chromosome can e denoed wih he ordered 1 pair η ( X, Y ) = ( X ( λ), Y ( k) ), where, λ ( η) 1 he pixel in posiion ( i, j) Y ( η) = X and. Each segmen P j is characerized y he mean value, mv j, of he gray levels: mv The finess funcion: j = δ Pj I ( X ( η) ) P ( ) ( η) = ρ η, mv 1 X ( η ) j 1 (3) f (4) The similariy funcion, ρ, compued eween a given chromosome η ( X,Y ) and he corresponding segmen P λ. The classical single poin crossover wih i muaion has een used o evolve he sysem. Random laels are assigned o he saring populaion of chromosomes. The geneic operaor and he selecion process are applied unil a haling condiion which is ased on he K convergence of he oal variance ( Vr = σ ( k) ) is saisfied: Hal compuaion if V 1 V ε, where, ( k) r r k σ is he inernal variance of he cluser k a he ieraion and ε 0, value of ε is deermined y he ε min r 1 r. The condiion ε = 0 is heurisics ( V, V ) Fig. 4 Edge map using GAs edge deecor for Fig.1 5. Comparison for Canny and GAs edge deecion of colony images The Canny operaor works in a muli-sage process. Firs of all he image is smoohed y Gaussian convoluion. Then a simple 2-D firs derivaive operaor is applied o he smoohed image o highligh regions of he image wih high firs spaial derivaives. Edges give rise o ridges in he gradien magniude image. The algorihm hen racks along he op of hese ridges and ses o zero all pixels ha are no acually on he ridge op so as o give a hin line in he oupu, a process known as non-maximal suppression. The racking process exhiis hyseresis conrolled y wo hresholds: TI and T2 wih TI > T2. Tracking can only egin a a poin on a ridge higher han TI. Tracking hen coninues in oh direcions ou from ha poin unil he heigh of he ridge falls elow T2. This hyseresis helps o ensure ha noisy edges are no roken up ino muliple edge fragmens. Comparing o Canny edge deecion mehod, he edge deecion mehod using GAs has some feaures as follows: (i) I is effecive o he edge deecion for he exure image wih randomness ecause of no using he pixel daa u using he local exure feaure, and he arrangemen of he edge regions is accomplished ased on he simple idea ha he shores individual wihin he candidae edge regions is he opimum edge regions. (ii) To decrease and eliminae noise of he colony images, i should increase he value of Canny edge deecor. Bu for he edge deecion mehod using GAs, i should

5 IJCSNS Inernaional Journal of Compuer Science and Nework Securiy, VOL.6 No.9A, Sepemer increase he lengh of he iniial populaion. (iii) The processing ime of his mehod is a lile more han ha of he Canny edge deecion mehod. 6. Conclusions and fuure work Tradiional edge deecors mehods such as Roers Cross, he Soel Operaor and Prewi operaor failed o perform adequaely in such applicaions due o he noisy naure of remoely sensed daa. They are no ale o deec he edges of he ojec while removing all he noise in he image. The implemened Canny edge deecor presened he es performance oh visually and quaniaively ased on he measures such as mean square disance, error edge map and signal o noise raio. Using he implemened Canny edge deecor as an enhancemen ool for remoe sensing images, he resul was rous and achieved a very high enhancemen level. The advanage of he edge deecion mehod using GAs is inelligen. I is effecive o he edge deecion for he colony image wih randomness ecause of no using he pixel daa u using he local exure feaure, and he arrangemen of he edge regions is accomplished ased on he simple idea ha he shores individual wihin he candidae edge regions is he opimum edge regions. The fuure work is o decrease he processing ime of he edge deecion mehod using GAs. Thresholding, IEEE Trans. Medical Imaging, vol. 22, pp , Sep [5]. J. Canny, A Compuaional Approach o Edge Deecion, IEEE Trans. Paern Analysis and Machine Inelligence, vol. 8, pp , A [6]. Yoshimura, M. and Oe, S., Edge deecion of exure image using geneic algorihms SICE '97. Proceedings of he 36h SICE Annual Conference. Inernaional Session Papers,29-31 July 1997 Page(s): [7]. Lo Bosco, G. A geneic algorihm for image segmenaion, Image Analysis and Processing, Proceedings. 11h Inernaional Conference on,26-28 Sep Page(s): Wang Luo received he B.S. degrees in Jiangxi Universiy of science and echnology in During , he worked in Jinggangshan Universiy o each and research. He is now a suden of School of Elecronic Engineering Universiy of Elecronic Science and Technology of China o achieve he M.S. degree. His research ineres is image processing. Acknowledgmen Thanks o Prof. Wang for his valuale advice. References [1]. S. Malla, A Wavele Tour of Signal Processing. Academic Press, [2]. Y. Xu e al., Wavele Transform Domain Filers: A Spaially Selecive Noise Filraion Technique, IEEE Trans. Image Processing, vol.3, pp , Nov [3]. B.M. Sadler and A. Swami, Analysis of Muliscale Producs for Sep Deecion and Esimaion, IEEE Trans. Informaion Theory, vol. 45, pp , Apr [4]. P. Bao and L. Zhang, Noise Reducion for Magneic Resonance Image via Adapive Muliscale Producs