Proceedngs of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lsbon, Portugal, June 6-8, 2005 (pp285-20) Novel Intellgent Edge Detector for Sonographcal Images Al Rafee *, Mohammad Hasan Morad **, Mohammad Reza Farzaneh *** *- Islamc Azad Unversty of Kazeroon; Kazeroon Iran **-Amrkabr Unversty; Tehran, Iran ***-Shraz Medcal Unversty; Shraz, Iran. Mal Address: Apartment 2 No. Sarbaz Blvd, Havabord, Shraz, Fars, Iran Abstract--Most mage processng, such as mage regstraton, mage segmentaton, regon separaton, object descrpton, and recognton, use edge detecton as a preprocessng stage. Real ultrasound mages, such as sonography mages, can be corrupted wth speckle nose. The real problem s how to extract the edges and smultaneously preserve mage detals. In ths paper a new genetc-neuro-fuzzy system s suggested for edge detector n ultrasound mages. The compettve neural network (NN) s used for ths system. Data processng wll be done by a wnner-take-all competton process s appled to subnetworks n NN and neurons n each subnetwork. The fuzzy transformer system s used to convert the neghborhood wndow of nput pxels to three decson fuzzy parameters. The on-lne genetc algorthm (OGA) s used to optmze and regulate the system parameters. A bnary pattern of neghborhood wndow s obtaned based on wnner subnetwork and neuron. After detectng the frst set of edge pxels, next structural algorthm wll be appled accordng to the locaton of edge pxels to elmnate some of the nosy edges and add some weak real edge pxels. System performance s compared wth the standard methods such as Sobel and zero-crossng edge detector. Results show that the genetc-neuro-fuzzy edge detector s a powerful edge detector, whose performance s better than standard edge detectors. Index Terms: Genetc-neuro-fuzzy, Compettve neural network, Edge detecton, Sonography mages, I. Introducton THE nonnvasve nature, low cost, portablty, and real-tme mage formaton make ultrasound (US) mage an essental tool for medcal dagnoss. Most ultrasound mage processng applcatons, such as mage regstraton, mage segmentaton, regon separaton, object descrpton, and recognton, use edge detecton as a preprocessng stage for feature extracton. Ths magng modalty, when used nonnvasve, allows hgh acquston rates and provdes mages n real-tme, but the mages s corrupted by a hgh level of speckle nose [6]. The problem of solatng ntensty changes n US magery s exacerbated by the presence of speckle, whch appears as a jumble of randomly placed brght and dark spots. Ths nose makes t dffcult to accurately dentfy edges, snce n some regons the nose produces artfcal edges, whle n other regons there are no echoes present and the edges seem ambguous. In such low-qualty mages (whch are very common n US magng), generc algorthms do not dentfy the border accurately. Several algorthms have been reported, whch could help dentfy edges n US mages [2][8][]. The golden standard algorthm for detecton of edges n mages was reported several years ago by Canny [7]. In that case, the edge s defned as a step functon embedded n whte nose. But n US mage data, the nose s speckle, whch has a hgh degree of correlaton wth the data. The edge cannot be descrbed by a step functon, and the dfference n the average gray levels of the varous regons s hgh. There have also been many studes of edge detecton wth learnng models that mmc one style or the other. Ths class ncludes, for example, computatonal neural networks[][3] [][3][][22][24], fuzzy reasonng systems [4][5][0][2][7][20][2][23] and neuro- fuzzy systems[4]. The more recent past technques concerned wth NNs have been nspred by features of NNs such as fault tolerance, computatonal smplcty, capablty to learn from examples for determnng correct threshold and ablty to process n a hghly parallel fashon that yeld a rch varety of edge mages. Ho [0], Russo [7] and Tzhoosh [2] suggested several models of fuzzy edge detector. Fuzzy edge detectors are flexble and robust methods, whle heurstc membershp functons, smple fuzzy rules and many nterference methods can be used n these systems. Lu and Wang [4] used a fuzzy neural network for edge detector that ncludes two stages: adaptve fuzzfcaton and detecton. The man dea n ther system s dvson nput patterns to 8 groups and classfes these patterns to edge or non-edge classes. They clamed that ths edge detector could be acts very well n addtve noses.
Proceedngs of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lsbon, Portugal, June 6-8, 2005 (pp285-20) Consderng the drawbacks of the edge detector systems mentoned, we have constructed our edge detecton method for ultrasound mages based on an ntellgent system. Ths system s composed of neural network that s the man structure of edge detector system, fuzzy system that s used for solvng the ambguty problems n edge defnton and genetc algorthm that s an optmum algorthm n NN learnng and settng the system structure. There are two problems that are solved by usng the ntellgent system: thresholdng problem n edge varaton range and presence of nosy edges n mages. Neuro-fuzzy network uses the learnng ablty of the neural networks, for whch the form of nformaton n ths system s fuzzy. The on-lne genetc algorthm (OGA) s used to optmze and regulate the system parameters [5][6][8]. Ths paper organzed as follows. Secton II contans the genetc-neuro-fuzzy edge detecton system; Secton III descrbes the fnal results and ther comparson to the other flters. Fnally, Secton IV contans our conclusons II. Genetc-neuro-fuzzy system Our edge detector system s shown n Fg.. Ths edge detector s based on a 3x3 neghborhood wndow, whch s the nput for edge detector system. The followng three parameters can be obtaned from the neghborhood wndow by usng fuzzy converter system; X : Fuzzy means of gray levels n neghborhood wndow. X mn : fuzzy means of gray levels lower than X n neghborhood wndow. X max : fuzzy means of gray levels more than X n neghborhood wndow. Fg. : Genetc-neuro-fuzzy edge detector. Fuzzy system s used to weght each pxel, based on ts gray level to obtan the above parameters. Fuzzy set has two members and ts membershp functon s trapezodal. Membershp functon for each wndow s defned adaptvely based on ts mean values. Maxmum operator s used for fuzzy nterference. X, X mn and X max are calculated below: X mn = 0 I( X) = X I( = = I( XfX X X X max -- 0, I( X) = X I( = = I( XpX X X X Xmn+ Xmax = X = X= (3) 2 X Parameter s used to select the wnner subnetwork and X mn and X max are for selectng wnner neuron n ths subnetwork. () (2) A. Context pattern n edge detector Ths system archtecture arouse from our observaton that n edge detecton, t would be more effectve to adapt multple sets of thresholdng decson parameters correspondng to dfferent local contexts. The dea behnd ths scheme s that each subnetwork s assocated wth an edge template corresponded to a dfferent context and each neuron n the subnetwork encodes varaton of edge prototypes under correspondng background elmnaton. A pattern of ths categorzaton method s shown n Fg. 2. P parameters show the central pont of each context (correspondng to each subnetwork) n ths fgure. These parameters are correctable and wll be optmzed n NN tranng steps based _ on X parameters n each wndow. In tranng steps _ X wll be calculated for each wndow and the subnetwork wth a closer P value to _ X and n the range [X mn X max ] wll be selected and the subnetworks P value s corrected. B. Neural network The compettve neural network s used for ths system. Data processng wll be done by a wnner-take-all competton process s appled to subnetworks n NN and neurons n each subnetwork. Ths process s done by use of nput neghborhood wndow. Each neuron s correspondng to the threshold value for edge detecton. Two weghts are defned for each neuron, that threshold and gray level change ranges are shown by the dfferent between these weghts. Each subnetwork consst two neurons n order that one of the neurons assocate to the local prototype for weakly edges and the other =
Proceedngs of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lsbon, Portugal, June 6-8, 2005 (pp285-20) assocate wth prototype for strongly edges. Therefore ths s a form of hysterss thresholdng that s used by more edge detectors (Fg. 3). For each nput neghborhood wndow, the X, X mn and X max parameters are obtaned and wnner subnetwork s selected. Dstance between parameters s adopted to subnetwork selecton as shown below: n _ d = mn ( X ) j =,.., n (4) j = P d j s mnmum dstance and j s wnner subnetwork. Each subnetwork conssts two neurons and each neuron ncludes two weghts that s defned as follows: W j = [ W j W j2 ] j =,2 (5) The wnner-take-all nature of the competton process also favors that only the local wnner wthn each subnetwork s allowed to update ts weght vector. The local neuron output s evaluated n term of the Eucldean dstance between the edge prototype and the current edge, whch s defned as follows: 2 2 n k = mn X W j, X = [X X max ], k =,2 (6) mn = Xn k s mnmum Eucldean dstance and k s wnner neuron number. C. Tranng stage Ths NN has 6 subnetworks, whch ths number s selected based on gray level gradent correspondng to edges (about 20) and varaton range of edge gray level (about 40). These values can be sensed vsually. Network parameters are frst selected randomly then updated n tranng stages accordng to the nput mages. As there are 4 weghts and one P parameter for each subnetwork, 24 parameters exst for NN. Onlne genetc algorthm (OGA) s used for tranng network. OGA algorthm s a sngle-member algorthm. Each generaton has a queen (member) and just mutaton operator produces next generaton. Genetc strng ncludes subnetwork P parameters and neuron weghts. P parameters s an 8-bts number (for 256 gray level) and neuron weghts are each an 8-bts number (4 weghts n each subnetwork); totally a genetc strng s a 40-bts as shown n Fg. 4. There are 6 genetc strngs n NN, whch are optmzed ndepently by wnner-take-all process. The nput mage s scanned from top to down and left to rght; neghborhood wndow s performed for each pxel. Neghborhood wndow s fed nto the edge detector system and the NN weght and P parameters are updated. In ths wndow X, X mn and X max are calculated and wnner subnetwork and also the wnner neuron n ths subnetwork are selected to update ther parameters. Fg. 2: An example of background elmnaton categorzaton Mutaton operator s appled by selectng random bts n genetc strng and complementng them. The new subnetwork and neuron parameters create by ths new strng (son). If the ftness functon of ths subnetwork s better than before, the new generaton s consdered as the queen otherwse t s rejected and the prevous queen s used to generate new genetc strng. The ftness functon used for P parameters s the nverse of the dstance between X and ths parameters and the same functon used for weghts s the nverse of Eucldean dstance between weght vector and X=[X mn, X max ]. Fg. 3: subnetwork structure. Fg. 4: Genetc bnary strng for weghts and P parameter. Accordng to the lmted nput varatons, the network wll be optmzed by at last 20 generatons. After tranng steps the nput mage s entered to the edge detector system once agan and a bnary pattern s obtaned for each neghborhood wndow. In ths patterns exstence or nexstency of edges can be seen vsually. The edge patterns are fed nto the edge set to use n recognton stages. Bnary patterns are created by dvdng the neuron gray level range nto the two equal sectons so that every pxel n the upper
Proceedngs of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lsbon, Portugal, June 6-8, 2005 (pp285-20) area s set to and the others set to 0. The followng relaton shows ths transformaton: 0 < 2 = X W k X W k b (7) otherwse X s the pxel n neghborhood wndow and b s the bnary pxel. 7 others bnary patterns can be created and entered to edges set to elmnated the rotatonally effects n edge detecton, by 45 clockwse rotaton as shown an example n Fg. 5. Fg. 5: An example for man bnary and 45 degree rotated patterns D. Recognton stage In system test process, new mages are appled to the system, for each pxel a neghborhood wndow s entered to the fuzzy transformer. In ths wndow X, X mn and X max values are calculated and by Equatons (4) and (5) wnner subnetwork and neurons are selected. If the followng condton satsfes, ths pxel s suggested as the edge: a) X X mn mn( w w 2, w 2 w 22 ) max (Dstance more than low threshold). b) The nput bnary pattern exsts n edges set. Bnary mage can be created by on-edge pxels settng to one and off-edge pxels settng to zero n whch context and edges are completely obvous. E. Post processng After detectng the frst set of edge pxels, next process wll take effect to elmnate some of the nosy edges and add some weak real edge pxels. So a structural algorthm wll be appled accordng to the locaton of edge pxels. Ths algorthm that s called thnnng, s performed by the bnary post-processng follows a few smple rules, whch remove spurous or unwanted edge ponts and add n edge ponts where they should be reported but have not been. They fall nto three categores; those removng spurous or unwanted edge ponts, those addng new edge ponts and those shftng edge ponts to new postons. III. Expermental results Input mages n ths system are ultrasound mages that are corrupted wth speckle nose. Ths nose has a hgh correlaton wth the man mages and s a multplcatve nose. Genetcneuro-fuzzy edge detector s used n ths paper. Fuzzy system acts as an nput converter that converts neghborhood wndow to a 3 fuzzy parameters X, X mn and X max. These parameters select the subnetwork and the neurons n ths subnetwork correspondng wth the nput neghborhood wndow. The NN used here s a compettve network that ncludes 6 subnetworks and each subnetwork represents a gray level range. Ths network structure covers the edge varaton n dfferent contexts. There are 2 neurons In each subnetwork that one of them corresponded to the low threshold and the others one corresponded to hgh threshold. Therefore a hysterss model of threshold s created n ths edge detector. A bnary pattern of neghborhood wndow s obtaned based on wnner subnetwork and neuron. In case ths bnary pattern exsts n edges reference set and the dfference between X max and X mn s more than low threshold, the central pxel wll be assgned to edge pxels. The structural postprocessng wll be done to elmnate nosy edges and add real weak edges. Ultrasound mages have 256 gray level and best crtera for edge detecton performance s vsual observaton. System performance s compared wth the standard methods such as Sobel, zero-crossng and Canny edge detector. The genetc-neuro-fuzzy edge detector was appled to sonography mages. A typcal Bowel mage that used here s depcted n Fg. 6(a). The genetc-neuro-fuzzy edge detector output s dsplayed n fg. 6(b). The results are compared wth the Sobel operator and the zero-crossng edge detector that s shown n Fg. 6(c) and. 6(d). The Canny output also s gven and depcted n Fg. 6(e). As shown n Fg. 6 the genetc-neurofuzzy edge detector s a powerful edge detector, whch ts performance s better than standard edge detectors. In Fg. 7(a), a sample renal mage and n Fg. 7(b) the nosy verson that s corrupted by speckle nose (0. varance) s depcted. The genetc-neuro-fuzzy output s depcted n Fg. 7(c). The Sobel, zero-crossng and Canny output are shown n Fg. 7(d), 7(e) and 7(f).
Proceedngs of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lsbon, Portugal, June 6-8, 2005 (pp285-20) IV. Concluson We have suggested a new genetc-neuro-fuzzy system for edge detector n ultrasound mages. The compettve NN s used for ths system. The fuzzy converter system s used to convert the nput pxels to decson fuzzy parameters. The OGA algorthm s used to learn the system parameters. A set of bnary patterns of edges s used for edge detecton. After detectng the frst set of edge pxels, the postprocessng algorthm wll be appled to elmnate the nosy edges and add real weak edges. System performance s compared wth the standard methods such as Sobel, zero-crossng and Canny edge detector. A malgnant tumor has been llustrated n fgure 6. But how much s the extenson and nvason of ths tumor? By comparng the revealed borders and also co-observaton of fg. a and b t can be shown that rght border of tumor extends up to the edge of pcture n fgure b but t seems that rght border of tumor has several centmeter dstance wth the edge of pcture a. The real borders of tumor could not be shown n none of fgures c, d and e. Sonographc shadow of a kdney s shown n fgure 7. Is there any cyst nsde ths kdney? It s dffcult to confrm or rule out ths ssue by usng fgures a and b. It would be a hgher possblty of renal cyst by vrtual edges n fg c and e although t s not true. The archtecture s completely dsfgured n fgure d. Fg. f s clearly revealng how an apparent renal cyst on a partcular angle could be cleared as normal renal tssue by edge clarfcaton. Results show that the performance of genetc-neuro-fuzzy edge detector s better than standard edge detectors. References [] I.N Azenberg, N.N Azenberg, J. 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Proceedngs of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lsbon, Portugal, June 6-8, 2005 (pp285-20) fuzzy logc approach, n Proc. 2nd IEEE Conf. Fuzzy Syst., Pscataway, NJ, pp. 600 605, 3. [24] Y.Uchyama,M. Haseyama, H. Ktajma., Hopfeld neural networks for edge detecton, The 200 IEEE Internatonal Symposum on Crcuts and Systems, vol. 3, pp. 608-6, 6- May 200. WA,. Fg. 6: a) Man Bowel mage, b) Genetc-neuro-fuzzy edge detector output, c) Sobel operator s output, d) Zero-crossng edge detector output, e) Canny edge detector output. Fg. 7: a) Man Renal mage, b) mage corrupted by 0. speckle nose. c) Genetc-neuro-fuzzy edge detector output, d) Sobel operator s output, e) Zero-crossng output, f) Canny edge detector output.