Automatic Detection of Malignant Melanoma using Macroscopic Images ABSTRACT

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1 Originl Article Automtic Detection of Mlignnt Melnom using Mcroscopic Imges Mrym Rmezni, Alirez Krimin, Pymn Mollem 1 Deprtments of Biomedicl Engineering, 1 Electricl Engineering, Fculty of Engineering, University of Isfhn, Isfhn, Irn Sumission: Accepted: ABSTRACT In order to distinguish etween enign nd mlignnt types of pigmented skin lesions, computerized procedures hve een developed for imges tken y different equipment tht the most ville one of them is conventionl digitl cmers. In this reserch, new procedure to detect mlignnt melnom from enign pigmented lesions using mcroscopic imges is presented. The imges re tken y conventionl digitl cmers with sptil resolution higher thn one megpixel nd y considering no constrints nd specil conditions during imging. In the proposed procedure, new methods to weken the effect of nonuniform illumintion, correction of the effect of thick hirs nd lrge glows on the lesion nd lso, new threshold sed segmenttion lgorithm re presented. 187 fetures representing symmetry, order irregulrity, color vrition, dimeter nd texture re extrcted from the lesion re nd fter reducing the numer of fetures using principl component nlysis (PCA), lesions re determined s mlignnt or enign using support vector mchine clssifier. According to the dermtologist dignosis, the proposed processing methods hve the ility to detect lesions re with high ccurcy. The evlution mesures of clssifiction hve indicted tht 13 fetures extrcted y PCA method led to etter results thn ll of the extrcted fetures. These results led to n ccurcy of 82.2%, sensitivity of 77% nd specificity of 86.93%. The proposed method my help dermtologists to detect the mlignnt lesions in the primry stges due to the minimum constrints during imging, the ese of usge y the pulic nd nonexperts, nd high ccurcy in detection of the lesion type. Key words: Clssifiction, mlignnt melnom, melnom dignosis, skin lesions INTRODUCTION Melnom is mlignnt pigmented skin lesion which is the dedliest type of skin cncer in the world. This cncer is the sixth most common cncer mong Americn men nd women nd is the min fctor of cncer deth in yers old women. Also, melnom is the most common type of cncer in yers old men in Austrli nd New Zelnd. [1] In Irn, ccording to the 8877 cses of skin cncer in 2006, this type of cncer ws known s the first cncer. [2] On the other hnd, moles tht re nturl prts of the skin re enign types of pigmented skin lesions. Chrcteristics of oth enign nd mlignnt pigmented skin lesions re similr which mkes differentiting etween them chllenging prolem. [3] Dermoscope lens nd conventionl digitl cmer re the most commonly used equipments tht re used to investigte chrcteristics of pigmented skin lesions. The usge of ech one of these equipments hs dvntges nd disdvntges. The visuliztion of susurfce microstructures of the epidermis nd upper dermis nd uniform illumintion re mong the enefits of dermoscopic imges ut, on the other hnd, the dermoscopy lens is not pulicly ville. [4,5] While nowdys, conventionl digitl cmers with sptil resolution higher thn one megpixel re widely used y the generl pulic nd the tken imges which re clled mcroscopic or clinicl imges re nonuniformly illuminted. Figure 1 shows mcroscopic nd dermoscopy imges of n invsive melnom. In this figure, the differences etween visile chrcteristics of the two mentioned types of imge, is cler. So, vrious computer processing techniques must e used for their nlysis. [3] The ultimte gol of procedures tht re developed to distinguish etween enign nd mlignnt pigmented skin lesions is simplicity of pplying y nonexperts nd the generl pulic. Hence, such procedures should e developed for mcroscopic imges with minimum constrints of imging conditions. Some of these conditions include the usge of specil resolution cmer, considering predetermined distnce etween cmer nd skin surfce, nd the usge Address for correspondence: Dr. Alirez Krimin, Deprtment of Biomedicl Engineering, Fculty of Engineering, University of Isfhn, Isfhn, Irn. E mil: krimin@eng.ui.c.ir Vol 4 Issue 4 Oct-Dec

2 to the feture set which used in the previous study. In this wy, the proposed procedure in tht study resulted in 100% sensitivity, 97.78% specificity nd 99.34% ccurcy. [9] The dtse of the mentioned studies ws limited due to the conditions nd constrints, which noted previously. This disdvntge prevents the proposed procedures from eing pproprite to e pplied on pulicly ville equipments tht re the ultimte gol of proposing these procedures. Figure 1: An invsive melnom () Mcroscopic imge, () Dermoscopic imge of flsh light during imging. Up to now, studies in this re hve een done ssuming the ove conditions nd constrints re pplied on the dtses. [6 9] In 2009, study ws done y Alcón et l. in which the lgorithm to detect mlignnt melnom from enign lesions y the usge of skin lesion mcroscopic imges is proposed. In this study, for lesion re segmenttion, first the elimintion of the low frequency sptil component of the imge ws used for ckground correction, nd then thresholding sed method which ws inspired y Otsu s lgorithm, ws pplied to segment the lesion re. By considering ABCD criteri, 55 fetures were defined nd extrcted from the determined lesion re. Then correltion sed feture selection method nd doost clssifier were used s feture selection step. In this lgorithm, one decision support prt ws dded which led to the usge of the personl informtion including skin type, ge, gender nd prt of the ody long with the output of imge clssifier. Finlly, 86% ccurcy, 94% sensitivity nd 68% specificity hve een chieved. [6] In 2010, Christensen et l. proposed procedure in which morphologicl opertors were used for thick nd thin hirs removl, pre nd postprocessing. Otsu s thresholding lgorithm ws pplied on lue chnnel of red, green nd lue (RGB) color spce loclly to determine the lesion re, nd then, 9 fetures descriing the overll shpe, order nd color distriution were extrcted. A prediction model ws constructed sed on sttisticl nlyses of the lgorithm outputs. Finlly y pplying n optiml threshold on output index score, 77% ccurcy ws chieved. [7] In 2011, the procedure is presented y Cvlcnti et l. in which shdow ws estimted y djusting two degree qudric polynomil on norml skin nd its effect ws ttenuted y removing this plne from the imge. To determine the lesion re, new three chnnel imge ws defined, nd thresholding method inspired y Otsu s lgorithm ws pplied on. Then y the usge of 52 extrcted fetures, which were grouped in ABCD criteri fetures, nd two k nerest neighorhood nd decision tree clssifiers in two modes, the lesion type ws predicted. Finlly, ccurcy of 96.71%, sensitivity of 96.26% nd specificity of 97.78% hs een otined. [8] In 2013, Cvlcnti et l. introduced 12 fetures sed on the vlues of eumelnin nd pheomelnin of the lesion nd dded them In this reserch work, new procedure to detect mlignnt melnom from enign pigmented skin lesions using mcroscopic imges tken y conventionl digitl cmer with sptil resolution higher thn one megpixel is presented, in which there is no constrint nd specil condition during imging of the used dtse. In this procedure, new method in order to weken the effect of nonuniform illumintion nd lso, new threshold sed lgorithm in order to segment lesion re is descried nd pplied on the dtse. Then, fter introduction nd pplying new methods to correct the effect of thick hirs nd lrge glows on the lesion, 187 fetures which indicte symmetry, order irregulrity, color vrition, dimeter nd texture re extrcted. The numer of fetures is reduced using principl component nlysis (PCA) lgorithm nd the result is used for predicting the type of lesion s enign or mlignnt using support vector mchine (SVM) clssifier. METHODS The proposed procedure hs three stges in order to detect mlignnt melnom from enign pigmented lesions. The first stge is preprocessing which includes removing effects of mcroscopic imges rtifcts nd determining lesion re with high ccurcy. In the second stge, descriptor fetures of lesions re extrcted nd in the third stge which is clled the clssifiction stge, optiml fetures re determined nd used to predict the type of lesions. Dtse The used dtse in this study is set of 282 mcroscopic imges of pigmented skin lesions which hd een collected from severl online dermtology tlses such s dermnet, dermis nd dermquest tlses. [10 16] This set includes RGB imges of 149 enign lesions nd 133 mlignnt which hve vrious dimensions of to pixels. Whole re of the lesion in ll of the imges is visile, ut lesion is not necessrily in the middle of the imge nd cn e connected to imge edges. These imges re tken y conventionl digitl cmers with different sptil resolutions which re >1 megpixel. There ws no need to dhere to predetermined distnce etween the cmer nd skin while imging nd in some cses, flshlight is used. Thus, the used dtse in this study hs the lest restrictions nd requirements for imging. 282 Vol 4 Issue 4 Oct-Dec 2014

3 Preprocessing At this stge, the effects of prt of rtifcts in mcroscopic imges, including impct noise, skin lines, fine hirs, skin stins nd smll glows nd reflections re removed y pplying medin filter with msk size which is clculted using Eq. 1. [17] ( ) n floor 5. M 768. N 512 (1) = ( ) ( ) In this eqution, msk size n is determined for n M N imge nd the floor function round down the result to the next integer. Then, in order to weken the effect of nonuniform illumintion or shdow, imge of originl RGB color spce is converted to hue, sturtion nd vlue (HSV) spce ecuse shdow effect in Vlue chnnel re more visile thn other chnnels nd spces. Then, ecuse of insensitivity of the proposed method to the loction of the lesion, smpling from the norml skin is done twice s follows nd s shown in Figure 2: Smpling from the four corners of V chnnel in squres y tking into ccount the mrgin of 10 pixels from imge edges Smpling from frme with width of 40 pixels in sidelines of V chnnel y tking into ccount distnce of 10 pixels from imge edges. In this wy, two sets of vlues corresponding to the pixels of helthy skin re cptured. Then 2 s nd 3 polynomil functions, which re defined in equtions 2 nd 3, respectively, hve een considered nd re dopted dpted on these two sets of smples y the lest squres method. Thus, four different plnes were estimted which represent four vrious modes of illumintion distriution on the imge with respect to the reltive re of the lesion in imge, loction of lesion on ody nd the wy of lighting while imging. Then four V chnnels which hve uniform illumintion re otined y dividing the originl V chnnel on these four plnes. Figure 3 shows the four estimted plnes for skin lesion imge nd the result of elimintion of ech one from the imge. If ech one of these four processed V chnnel would e used for the following opertions, imges with uniform illumintion re otined tht their helthy skin color is right nd different from the originl imge s cn e seen in Figure 3. In order to retrieve true color of the skin, Eq. 4 is pplied on ech of processed V chnnels. [9] V new Vproc ( x, y) µ V ( x. y)= µ Vproc orig In this eqution, V proc is the processed V chnnel, V orig is the originl V chnnel, μ represents men of the respective chnnel nd V new is new V chnnel. Then mong new nd originl V chnnels, n imge which hs the lest instility level, nd therefore entropy, is (4) 2 2 Q ( x, y)= P. x + P. y + P. xy + P. x+ P. y+ P (2) c Q ( x, y)= P. x + P. y + P. xy+ P. xy + P. x + P. y P. xy+ P. x+ P 9.y+ P (3) In the equtions 2 nd 3, P i (i = 1,, 6 for Q 2 nd i = 1,, 10 for Q 3 ) determines qudric function prmeter nd (x, y) is imge sptil loction. d e f g Figure 2: Smpling from the norml skin () From four corners, () From the frme Vol 4 Issue 4 Oct-Dec 2014 h Figure 3: () Smoothed imge of skin lesion, ( nd c) Results of dption of two degree polynomil function on the corners smples, (d nd e) Results of dption of three degree polynomil function on the corners smples, (f nd g) Results of dption of two degree polynomil function on the frme smples, (h nd i) Results of dption of three degree polynomil function on the frme smples i 283

4 chosen s the est V chnnel with uniform illumintion ecuse existence of shdow on the imge leds to incresed instility. This chnnel is replced to the originl V chnnel, nd the finl imge is converted from HSV color spce to RGB spce. Figure 4 shows the imge of skin lesion with shdow tht the proposed medin filter nd shdow reduction method were pplied on. The second step in the preprocessing stge is segmenttion of the lesion re from surrounding norml skin. For this purpose, new, simple nd ccurte segmenttion method, which is sed on thresholding technique is introduced in which the single chnnel imges contining determinnt fctors of lesion order mening color, illumintion nd texture re otined firstly. Red chnnel of the RGB color spce nd l* chnnel of CIEl*u*v* color spce represent color nd rightness informtion, respectively, nd the first component of principl components which re otined from pplying PCA method on the RGB imge indictes texture informtion. The reson of using red chnnel s color represent or is the fct tht ech ethnic group hs helthy skin color of reddish nd skin lesions re regions of skin with ltered color. The reson of using the first component is the fct tht this component contins mximum chnges in the imge, nd in skin lesion imges, mximum chnges s well s most of texture informtion occur on the lesion order. Seprtion of lesion from helthy skin is more effective y using one of the three mentioned single chnnel imges which re determined y exmining the histogrm informtion. In generl, histogrm of skin lesion imge hs two peks corresponding to helthy skin nd lesion re which whtever they re frther nd the vlley etween them is deeper, lesions re will e seprted with higher ccurcy from helthy skin. Therefore, single chnnel imge is selected which distnce etween peks of its smoothed histogrm using locl regression is mximum. Third threshold tht is the strting point of helthy skin Gussin distriution (level f ) Fourth threshold tht is the point with the lowest height etween lesion nd helthy skin distriution on the histogrm (level v ). Then the thresholds on the imge histogrm which hve the minimum distnces to ech other in terms of intensity level re selected nd the lrgest one of them which covers results of other selected thresholds is pplied on the optiml single chnnel imge. Since the shdow effect is corrected t first nd therefter, the threshold nd orders re determined; shdow will not e mistken y the lesion re nd cnnot ffect on the orders determintion. Figure 5 shows histogrm of the optimum gry scle imge of skin lesion imge with the four mentioned thresholds nd the results of pplying them nd the optiml one. In the histogrm of Figure 5, the first nd fourth thresholds completely mtches nd, therefore, re considered s the closest ones. Figure 5c shows results of using these two thresholds tht indictes the lesion oundries very ccurte. As cn e seen in Figure 5d g, the oundries of the second nd third thresholds show lrge errors, while the selected thresholds y segmenttion lgorithm led to the est results. Four different thresholds re defined nd clculted over the optimum single chnnel imge s follows: First threshold is clculted using Otsu thresholding lgorithm (level o ) Second threshold tht is the men vlue of lesion nd helthy skin distriution peks of the histogrm (level m ) d c e Figure 4: A skin lesion imge () The originl imge, () The processed imge fter pplying medin filter nd shdow reduction method f Figure 5: () Histogrm of the optiml gryscle skin lesion imge, () The preprocessed imge of skin lesion, (c) Finl result of segmenttion, (d) Determined oundries using the first threshold, (e) The second threshold, (f) The third threshold, (g) The fourth threshold g 284 Vol 4 Issue 4 Oct-Dec 2014

5 At the finl step of the preprocessing stge, the effect of thick hirs nd lrge glows nd reflections on lesion is corrected to improve the results of segmenttion lgorithm nd increse the ccurcy nd qulity of determined oundries. In order to correct the effect of thick hirs, ottom ht morphologicl trnsformtion is pplied nd ojects which their length to width rtio is >10 hve een removed. This opertion hs een implemented with the ssumption tht hirs hve long nd nrrow structures, while lesion hs ellipticl structure. If the imge hs lot of thick hirs, their remining detils on the order of lesion msk re corrected y pplying morphologicl opening opertor with circulr structurl element of size 3. Otherwise, this opertor is not required to e pplied. Following this, morphologicl closing opertor with the sme element s opening opertor is pplied on the whole imges which removes indenttions on the oundry cused y reflection of light from lines nd dents of skin surfce. Finlly, numer of pixels of ech oject in the imge is clculted nd the lrgest one is selected s the lesion msk. At the end, if flsh light is used, the effect of lrge glows nd reflections on imge will e corrected ecuse intensity level of lrge res on the imge or its sides re increses due to intense light of flsh. So the flsh light effect will e checked just on the lesion re, its effect on the lesion order will e corrected nd its effect on norml skin is not mtter in this study. For this purpose, imge is converted to cyn, mgent, yellow, nd key color spce nd Y (yellow) chnnel which cn show res of glows in the est wy is selected. Then ellipticl shped re which contins entire lesion nd prt of surrounding helthy skin is determined s it is descried in the following nd the imge is limited to it. For this purpose, the est fit ellipse is defined, nd length of its mjor nd minor xes is incresed to the size of lrgest Eucliden distnce of order lesion nd the defined ellipse, plus constnt vlue. One of the resons of the imge limiting is tht lesion re in more cses is much smller thn helthy skin nd y limiting imge to n ellipse, ccurcy of seprtion glows res on lesion will hs significnt improvement. In ddition, the mount of processed dt is lso reduced which leds to n incresed processing speed. Also, while defining n ellipse y incresing the lengths of ellipse xes nd dding constnt vlue to them, it is ensured tht its order is locted on the helthy skin ecuse ellipse order indictes helthy skin. To determine glow re, k mens clustering lgorithm is pplied once on the limited chnnel Y nd once more on the cluster with minimum center vlue, which is the output of the first run of the clustering lgorithm. In ech run of the lgorithm, the numer of clusters is selected equls to Vol 4 Issue 4 Oct-Dec 2014 the numer of smoothed histogrm peks of the input set, nd five sequentil itertions is performed. If the histogrm hs only one pek, the numer of clusters is considered to e equl two. From results of second clustering, wht is connected to the lesion order nd is not connected to ellipse order is selected s glow msk. At the end, OR comintion is pplied on lesion nd glow msks, nd the finl inry imge is otined fter pplying morphologicl closing nd filling opertors. Figure 6 shows the results of descried sequence for determintion of glow msk step y step. Figure 7 shows the result of pplying the descried preprocessing step on three different skin lesions chrcterized y the lck nd hving lot of thick hirs nd lrge glows on lesion re. Feture extrction In this study similr to the trditionl process of visul inspection, fter determining lesion re, set of min fetures is extrcted from the re nd re comined in order to distinguish etween enign nd mlignnt skin lesions. ABCD criteri re selected mong methods used to dignose melnom ecuse of mesurility of its fetures using the informtion contined in mcroscopic imges. These criteri hve four fetures of symmetry, order irregulrity, color vrition nd dimeter. [18] In ddition to these fetures, texture feture plys decisive role in distinguishing etween enign nd mlignnt lesions. Finlly, five groups of descriptors which re defined to mesure these fetures re extrcted nd comined. Fetures of symmetry group re sed on chrcteristics descripting center of grvity nd inerti moments of lesion, which ech one tries to mesure the lesion symmetry in the est wy. This group includes 32 fetures such s orienttion ngle, symmetry indices, [19 23] men squred error of nonoverlpping re with the respect to mjor xes, eccentricity, [6,24] equivlent dimeter, [6,8,25] circulrity indices, [6,8] excircle nd circumcenter index, sphericity index, [6] four fetures sed on res of oth sides of the d e Figure 6: () A skin lesion with lrge glow on lesion re nd the determined orders y lesion msk, () The limited Y chnnel, (c) The cluster with minimum center vlue (result of first run of clustering lgorithm), (d) Result of second run of clustering lgorithm, (e) Glow msk, (f) The determined orders y the finl inry imge c f 285

6 Figure 7: The originl (top) nd preprocessed (ottom) imge of skin lesion () Without hirs nd lrge glows, () With lot of thick hirs, nd (c) With lrge glows on lesion re c mjor xes, [8] normlized contour moments, [24] dimensionless moments, [26] extent index, elongtion index [8] nd re of ounding ox. Border irregulrity group is consisted of 34 fetures, which cn e ctegorized in sets sed on re nd perimeter of the lesion, irregulrity index, est fit ellipse, convex hull, grdient nd frctl geometry, nd re re, two perimeters, four fetures sed on rdius, [25] order irregulrity indices, [27,28] compctness index, [22] heywood circulrity index, men curvture, [7] est fit ellipse indices, [7,29] ulkiness index, [30] ending energy, re nd perimeter of convex hull, convexity index, [24] solidity index, [8] indenttion nd protrusion index, frctl dimensions [20,30] nd ten order resolution. [6,8,20] Some of the fetures hve different descriptions which re ll considered. Color vrition group comprises 72 fetures of RGB nd non RGB color spces components nd gry scle imge. Most descriptors of this group re sttisticl nd re extrcted from lesion msk which doesn t contin glows res. Moreover, pixels with vlues <70% of the mximum of ech chnnel re removed in clcultions relted to helthy skin to ensure tht there is no effect of hirs. Among the sttisticl chrcteristics of RGB color spce components nd gry scle imge cn e noted to the minimum, mximum, rnge of vlues, men, stndrd devition, coefficient of vrition nd vrince nd skewness, normlized stndrd devition, rtio of men vlues of RGB components, six sic colors counters, [6] reltive chromticity. [22] The sttisticl chrcteristics of non RGB color spce components re men nd stndrd devition. These spces include CIElch, [31] CIEl***, [32] HSI nd sphericl colour spce which is defined y three Br, ngle α nd ngle β components. [26] Lesion dimeter fetures contin 7 fetures of est fit ellipse dimeter, mjor dimeter nd the mximum distnce etween two nondjcent points on the lesion order. Lesion texture fetures re extrcted from gry level co occurrence mtrixes. These fetures include men nd rnge of vlues of 21 descriptors which re clculted for ech of the four co occurrence mtrix for four different orienttions of 0, 45, 90 nd 135 nd overll, descrie 42 fetures for lesion texture. Among the co occurrence mtrixes descriptors cn e noted to the uto correltion, contrst, correltion, cluster prominence, cluster shde, dissimilrity, energy, entropy, homogeneity, mximum proility, [33] vrince, sum verge, sum entropy, sum vrince, difference vrince, difference entropy, informtion mesures of correltion, [34] inverse difference, inverse difference normlized nd inverse difference moment normlized. [35] Typiclly, vlues of extrcted descriptors re t different rnges which if set in certin rnge leds to significntly improved clssifiction performnce. For this reson, vlues of descriptors re normlized using z score conversion nd Eq. 5. ( fi, j µ j) ( 3σ j)+ 1 Zi, j= (5) 2 In the ove eqution, f i, j is the mount of j th descriptor of i th imge nd µ j nd σ j re men nd stndrd devition of j th descriptor, respectively. This conversion ensures tht 99% of Z i, j vlues re in the rnge of zero nd one. Wht is out of this rnge is rounded to zero or one. [36] Clssifiction After the feture extrction stge, set of high dimensionl dt is otined which high numer of them is effective on the ccurcy nd required time for ccurte clssifiction. Moreover, extrction of this numer of fetures will led to high computtionl cost in terms of time nd storge. At this stge, the numer of fetures in order to chieve etter performnce will e reduced. For this purpose, the PCA, which is n unsupervised liner feture extrction method, is used. In the 286 Vol 4 Issue 4 Oct-Dec 2014

7 PCA, due to the different units in the feture set, the correltion mtrix is used insted of the covrince mtrix. After implementing this process nd clculting eigenvlues nd vrinces, set of principl components is otined which re rrnged in order to their ility in distinguishing etween enign nd mlignnt lesions. In order to determine the numer of fetures which leds to the est clssifiction results, the k numer of sorted fetures is determined, nd their efficiency is exmined during clssifiction. Finlly, the fetures with mximum efficiency re selected. [24,37,38] Clssifiction is the lst step in the computerized nlysis of pigmented skin lesions imges in which lesion is predicted s enign or mlignnt. According to the previous studies, SVM hve performed well in the field of skin lesions clssifiction. In ddition, this lgorithm hs vrious prmeters tht different dt models cn e seprted y chnging them. Therefore, SVM with rdil sis function kernel is used s the clssifier in this study. Rdil sis function kernel hs two prmeters of C nd γ which their optiml vlues re determined y grid serch on two sequences of C = {2 5, 2 4,, 2 9, 2 10 } nd γ = {2 8, 2 7,, 2 3, 2 4 }. During the grid serch procedure, ten fold strtified cross vlidtion is performed to evlute how well prticulr comintion of prmeters is. After the grid serch, the trget dtse is divided into two trining nd test sets. 70% of the dtse is used for trining nd the remining 30% formed the test set. In oth sets, the rtio of two enign nd mlignnt clsses is the sme. Then SVM clssifier with optiml prmeters is trined nd then tested on these two sets. In order to estimte the clssifiction error, this procedure is performed 100 times nd ech time y chnging the memers of trining nd test sets, nd the men nd stndrd devition of the following evlution criterion re clculted: Sensitivity: Percentge of ptients who hve een dignosed correctly s ptients. Sensitivity = TP ( TP + FN ) (6) Where TP nd FN represent the numer of ptients who hve een dignosed correctly s ptient nd incorrectly s helthy, respectively. [39] Specificity: Percentge of helthy people who re correctly dignosed s helthy. Specificity = TN ( TN + FP ) (7) Where TN nd FP represent the numer of helthy people who hve een dignosed correctly s helthy nd incorrectly s ptient, respectively. [39] Accurcy: Percentge of ptient nd helthy individuls who hve een dignosed correctly. [39] ( ) ( ) (8) Accurcy = TP+ TN TP FP TN FN The re under the curve (AUC): Receiver operting chrcteristic curve is the trce of TP rte versus FP rte nd AUC provides the expected performnce of prediction s numericl vlue. [40] The descried clssifiction procedure is implemented on the dtse with ll the extrcted fetures nd lso, on the k numer of fetures otined y the reducing method. Then the optiml feture set which hs the lowest numer of memers nd lso, the highest AUC vlue, is selected. RESULTS The results of the proposed methods in preprocessing stge hve een exmined y dermtologist. According to the medicl doctor dignosis, these methods detect the oundries of lesions with high ccurcy nd determine the lesion re with ccurcy of extent of 100% for the used dtse in this study. In the clssifiction stge, initil experiment ws conducted on the dtse with ll the extrcted fetures. The optiml prmeters which re found y the grid serch nd 10 fold cross vlidtion for this input set, hve vlues of (C*, γ*) = (32, ). Applying 100 times of SVM clssifier with these optiml prmeters on the trining nd test sets of 197 nd 85 memers respectively, leds to 81.13% ± 3.25% ccurcy, 75.66% ± 6.87% sensitivity, 86.14% ± 5.27% specificity nd 0.87 ± 0.03 re under the chrcteristic curve. Then, with the gol of reducing the computtion time nd incresing the efficiency of the clssifier, the clssifiction procedure runs on the k fetures otined y dimension reduction method. Due to the complexity of the prolem in this study, it does not seem tht smll numer of fetures hs ility to mke distinctions etween clsses very well. On the other hnd, the lrge numer of fetures my result in poor performnce of the clssifier. With these ssumptions, k rnges etween 5 nd 60. Figure 8 shows men vlues of the AUC of 100 times clssifiction with the optiml prmeters versus the suset size of the principl components which re otined y PCA. In this figure, it is oserved tht highest vlue of the AUC corresponds to the suset of principle components with size of 13, which hs the men vlue of The men nd stndrd devition of ccurcy, sensitivity nd specificity for this suset re 82.2% ± 3.57%, 77.02% ± 5.97% nd 86.93% ± 5.46%, respectively which re otined for optiml prmeters (C*, γ*) = (256, ). In Figure 9 which shows the men vlues of ccurcy, sensitivity nd specificity versus the size of principl components susets, the mentioned vlues cn e oserved. Vol 4 Issue 4 Oct-Dec

8 Figure 8: The men vlues of re under the curve versus the size of principl components suset Tle 1: The clssifiction results for ll fetures nd the optiml fetures determined y feture selection method Feture selection lgorithm Numer of fetures Tle 1 shows results of clssifiction for the optiml numer of fetures selected y the feture selection method nd lso, for ll the extrcted fetures. By compring the vlues listed in this tle, it is oserved tht vlues of evlution criteri re incresed using 13 principl components which re extrcted y PCA method thn 187 originl fetures which re extrcted from lesion re. As result, much smller numer of fetures cn led to etter clssifiction results. DISCUSSION AUC Accurcy* Sensitivity* Specificity* ± ± ±5.27 PCA ± ± ±5.46 *The results re listed s men±sd. SD Stndrd devition; AUC Are under the curve; PCA Principl component nlysis This pper presents new procedure for clssifying pigmented skin lesions s enign or mlignnt using mcroscopic imges, which re tken y conventionl digitl cmers with sptil resolution higher thn one megpixel. While imging the used dtse, ny constrints nd specific conditions re voided tht is n importnt difference etween this study nd the previous ones in this re nd mkes the proposed procedure pproprite for implementtion y pulic nd nonspecilists. In this study, new methods to enhnce the qulity of processing nd nlysis of mcroscopic imges of skin lesions hve een proposed; including new method which wekened effect of nonuniform illumintion on the imge in the est wy, new thresholding sed lgorithm which y review the existing informtion on the imge histogrm exploits Figure 9: The men vlues of ccurcy, sensitivity nd specificity versus the size of principl components suset extrctle informtion of the imge nd new method which corrects effect of thick hirs nd lrge glows on the lesion tht pper while imging using flsh light nd gretly increses ccurcy of the oundries set y the segmenttion lgorithm. In this study, 187 fetures representing symmetry, order irregulrity, color vrition, dimeter nd texture which re the mximum numer of extrctle fetures from the lesion re extrcted nd y using the PCA lgorithm, 13 optiml fetures re selected. Finlly, SVM clssifier predicts lesion types with ccurcy of 82.2%, sensitivity of 77% nd specificity of 86.93%. Becuse of dissimilrity etween the used dtses in this study nd the other ones, the chieved results cnnot e compred. However the ccurcy improved significntly ginst the 64% ccurcy of nked eye specilist, which is worthy conclusion. According to the dermtologist report, the proposed method in this reserch due to its sensitivity, ccurcy nd specificity my help dermtologists in detection the mlignnt melnom in more priority stges which my help their tretment more effectively. Moreover, if there ws ccess to the personlity nd imging informtion such s tumor site, ptient s skin nd eye color, the distnce etween cmer nd skin nd the lesion dimeter which is limittion for this procedure, the chieved results could e improved significntly. REFERENCES 1. Kutluy Z, Engin B, Serdroğlu S, Tüzün Y. Current mngement of mlignnt melnom: Stte of the rt. In: Vereecken DP, editor. Highlights in Skin Cncer. Istnul: InTech.; p Azly M, Rmezni R, Dounlu M, Sedighi Z, Diry A, Prtoie A. Ntionl Report of Cncer Registrtion Tehrn: Ministry of Helth nd Medicl Eduction; p Korotkov K, Grci R. Computerized nlysis of pigmented skin lesions: A review. Artif Intell Med 2012;56: Rigel DS, Russk J, Friedmn R. The evolution of melnom dignosis: 25 yers eyond the ABCDs. CA Cncer J Clin 2010;60: Wurm EM, Soyer HP. Scnning for melnom. Aust Prescr 2010;33: Alcón JF, Ciuhu C, Kte W, Heinrich A, Uzunjkv N, Krekels G, et l. 288 Vol 4 Issue 4 Oct-Dec 2014

9 Automtic imging system with decision support for inspection of pigmented skin lesions nd melnom dignosis. IEEE J Sel Top Signl Process 2009;3: Christensen JH, Soerensen MB, Linghui Z, Chen S, Jensen MO. Pre dignostic digitl imging prediction model to discriminte etween mlignnt melnom nd enign pigmented skin lesion. Skin Res Technol 2010;16: Cvlcnti PG, Schrcnski J. Automted prescreening of pigmented skin lesions using stndrd cmers. Comput Med Imging Grph 2011;35: Cvlcnti PG, Schrcnski J, Brnoski GV. A two stge pproch for discriminting melnocytic skin lesions using stndrd cmers. Expert Syst Appl 2013;40: Cohen BA, Cohen MR, Jnju SA, Hosler G, Wseem M. Dermtls Bet. Aville from: [Lst cited on 2014 Fe 02]. 11. Skin Disese Atls. Dermnet: Portsmouth, NH; Aville from: [Lst cited on 2014 Fe 02]. 12. Ntionl Cncer Institute. NCI Visuls Online. Aville from: [Lst cited on 2014 Fe 02]. 13. Okley A. DermNet NZ: The Dermtology Resource. Aville from: [Lst cited on 2014 Fe 02]. 14. Hexsel DM, Estern J, Feldmn SR, Spencer JM, Miychi Y, Arctingi S, Pndy AG. Personlised lerning nd teching resources for dermtologists tody nd tomorrow. Aville from: dermquest.com. [Lst cited on 2014 Fe 02]. 15. Silv SF, Clheiros DB. Dermtology Atls. Aville from: tlsdermtologico.com.r/. [Lst cited on 2014 Fe 02]. 16. DOIA Tem. Dermtology Informtion System. Aville from: [Lst cited on 2014 Fe 02]. 17. Celei ME, Kingrvi HA, Iytomi H, Aslndogn YA, Stoecker WV, Moss RH, et l. Border detection in dermoscopy imges using sttisticl region merging. Skin Res Technol 2008;14: Friedmn RJ, Rigel DS, Kopf AW. Erly detection of mlignnt melnom: The role of physicin exmintion nd self exmintion of the skin. CA Cncer J Clin 1985;35: Cheung K. Imge Processing for Skin Cncer Detection: Mlignnt Melnom Recognition [Mster s Thesis]. Cnd: Ntionl Lirry of Cnd; p Zgrou E, Brhoumi W. A prelimry pproch for the utomted recognition of mlignnt melnom. Imge Anl Stereology 2004;23: She Z, Liu Y, Dmto A. Comintion of fetures from skin pttern nd ABCD nlysis for lesion clssifiction. Skin Res Technol 2007;13: Prolin A, Herzer E, Jung CR. Semi utomted dignosis of melnom through the nlysis of dermtologicl imges. In: The 23 rd SIBGRAPI Conference on Grphics, Ptterns nd Imges (SIBGRAPI); Grmdo: IEEE; p Stoecker WV, Li WW, Moss RH. Automtic detection of symmetry in skin tumors. Comput Med Imging Grph 1992;16: Celei ME, Aslndogn YA. Content sed imge retrievl incorporting models of humn perception. In: Proceedings of Interntionl Conference on Informtion Technology: Coding nd Computing (ITCC 2004); 2004 April, 5 7: IEEE; p Mnouski AG, Mnios AG, Tsompnki EI, Pnyiotides JG, Tsiftsis DD, Kostki AK, et l. A simple digitl imge processing system to id in melnom dignosis in n everydy melnocytic skin lesion unit: A preliminry report. Int J Dermtol 2006;45: Kusumoputro B, Ariynto A. Neurl network dignosis of mlignnt skin cncers using principl component nlysis s preprocessor. In: The 1998 IEEE Interntionl Joint Conference on Neurl Networks Proceedings. IEEE World Congress on Computtionl Intelligence; 1998 My, 4 8. Anchorge, AK: IEEE; p Kjoelen A, Thompson MJ, Umugh SE, Moss RH, Stoecker WV. Performnce of AI methods in detecting melnom. IEEE Eng Med Biol Mg 1995;14: Mgloginnis IG, Zfiropoulos EP. Chrcteriztion of digitl medicl imges utilizing support vector mchines. BMC Med Inform Decis Mk 2004;4: Chng Y, Stnley RJ, Moss RH, Vn Stoecker W. A systemtic heuristic pproch for feture selection for melnom discrimintion using clinicl imges. Skin Res Technol 2005;11: Clridge E, Hll PN, Keefe M, Allen JP. Shpe nlysis for clssifiction of mlignnt melnom. J Biomed Eng 1992;14: X Rite, Inc. A Guide to Understnding Color Communiction. USA: X Rite; p Pltniotis KN, Venetsnopoulos AN. Color Imge Processing nd Applictions. Color Spces. Ch. 1. Berlin Heidelerg: Springer; p Soh LK, Tstsoulis C. Texture nlysis of SAR se ice imgery using gry level co occurrence mtrices. IEEE Trns Geosci Remote Sens 1999;37: Hrlick RM, Shnmugm K, Dinstein IH. Texturl fetures for imge clssifiction. IEEE Trns Syst Mn Cyern 1973;SMC 3: Clusi DA. An nlysis of co occurrence texture sttistics s function of grey level quntiztion. Cn J Remote Sens 2002;28: Aksoy S, Hrlick RM. Proilistic vs. geometric similrity mesures for imge retrievl. In: Proceedings of IEEE Conference on Computer Vision nd Pttern Recognition (CVPR); 2000 Jun, Hilton Hed Islnd, SC: IEEE; p Hn J, Kmer M. Dt Mining: Concepts nd Techniques. 2 nd ed. Sn Frncisco, CA, USA: Morgn Kufmnn Pulishers Inc.; p Murtgh F, Heck A. Multivrite Dt Anlysis with Fortrn, C nd Jv Code. Northern Irelnd: Queen s University Belfst, Astronomicl Oservtory Strsourg; p Zhu W, Zeng N, Wng N. Sensitivity, specificity, ccurcy, ssocited confidence intervl nd ROC nlysis with prcticl SAS implementtions. In: Proceedings of NESUG: Helth Cre nd Life Sciences; 2010 Nov, Bltimore, Mrylnd; p Hung J, Ling CX. Using AUC nd ccurcy in evluting lerning lgorithms. IEEE Trns Knowl Dt Eng 2005;17: How to cite this rticle: Rmezni M, Krimin A, Mollem P. Automtic Detection of Mlignnt Melnom using Mcroscopic Imges. J Med Sign Sence 2014;4: Source of Support: Nil, Conflict of Interest: None declred Vol 4 Issue 4 Oct-Dec

10 BIOGRAPHIES Mrym Rmezni received B.Sc. degree in electronics engineering from Isfhn University of Technology, Isfhn, Irn, in 2011, nd she received M.Sc. degree in iomedicl engineering from University of Isfhn, Isfhn, Irn, in E mil: m.rmezni85@yhoo.com Alirez Krimin received his B.Sc. degree in electronics engineering from Ferdowsi University, Mshhd, Irn. He lso received his M.Sc. nd Ph.D. degrees in nucler engineering in the field of medicine from Amirkir University of Technology, Tehrn, Irn. Furthermore He hs pssed successfully one yer fellowship reserch in the field of medicl physics in the L Spienz University, Rome, Itly under grnt of ICTP. In 2006, He joined the Deprtment of Biomedicl Engineering t University of Isfhn, Irn, s n Assistnt Professor, nd ws the lecturer of some courses such s medicl imging systems, Simultion nd its ppliction in medicine, rdition shielding, Dosimetry nd rdition detection, Biophysics nd medicl physics. Since Ferury of 2013, He ws successful to e s Associte Professor in University of Isfhn. He hs pulished more thn 130 reserch ppers in peer reviewed journls nd conferences. His reserch interests re: Imging systems Imge processing Dosimetry Rdiotherpy Monte Crlo simultion nd its pplictions in medicine. E mil: Krimin@eng.ui.c.ir Pymn Mollem received B.Sc. nd M.Sc. degrees in electronics engineering from Isfhn University of Technology, Isfhn, Irn, in 1992, nd Amirkir University of Technology, Tehrn, Irn, in 1996, respectively. He lso received PhD degree in electricl engineering from Amirkir University of Technology. In 2003, he joined the Deprtment of Electricl Engineering t University of Isfhn, Irn, s n ssistnt professor, nd ws promoted clude imge processing, mchine vision, neurl networks, pttern recognition, intelligent systems, nd rel time signl nd video processing. Since 2006, he hs een memer of the editoril ords of Mjlesi Journl of Electricl Engineering nd Mjlesi Journl of Multimedi Processing. He hs pulished more thn 235 ppers in peer reviewed journls nd conferences. E mil: P_mollem@eng.ui.c.ir Author Help: Online sumission of the mnuscripts Articles cn e sumitted online t For online sumission, the rticles should e prepred in two files (first pge file nd rticle file). Imges should e sumitted seprtely. 1) First Pge File: Prepre the title pge, covering letter, cknowledgement etc. using word processor progrm. All informtion relted to your identity should e included here. Use text/rtf/doc/pdf files. Do not zip the files. 2) Article File: The min text of the rticle, eginning with the Astrct to References (including tles) should e in this file. Do not include ny informtion (such s cknowledgement, your nmes in pge heders etc.) in this file. Use text/rtf/doc/pdf files. Do not zip the files. Limit the file size to 1 MB. Do not incorporte imges in the file. If file size is lrge, grphs cn e sumitted seprtely s imges, without their eing incorported in the rticle file. This will reduce the size of the file. 3) Imges: Sumit good qulity color imges. Ech imge should e less thn 4096 k (4 MB) in size. The size of the imge cn e reduced y decresing the ctul height nd width of the imges (keep up to out 6 inches nd up to out 1800 x 1200 pixels). JPEG is the most suitle file formt. The imge qulity should e good enough to judge the scientific vlue of the imge. For the purpose of printing, lwys retin good qulity, high resolution imge. This high resolution imge should e sent to the editoril office t the time of sending revised rticle. 4) Legends: Legends for the figures/imges should e included t the end of the rticle file. 290 Vol 4 Issue 4 Oct-Dec 2014