An Effective Thresholding Technique for Otsu s Method using Contrast Enhancement 1 A Sankar Reddy, 2 C Usha Rani, 3 M. Sudhakar

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1 Interntionl Journl Of Engineering And Computer Science ISSN:39-74 Volume 6 Issue 8 August 07, Pge No Index Copernicus vlue (05): 58.0 DOI: /ijecs/v6i8. An Effective Thresholding Technique for Otsu s Method using Contrst Enhncement A Snkr Reddy, C Ush Rni, 3 M. Sudhkr Assistnt Professor, Assistnt Professor, 3 Reserch Associte, AITS, JNTUA University, 3 VIT Chenni snkr55@gmil.com ch.ush86@gmil.com 3 mll.sudhkr05@vit.c.in Astrct Imge segmenttion is the fundmentl pproch of digitl imge processing. Among ll the segmenttion methods, Otsu method is one of the most successful methods for imge thresholding ecuse of its simple clcultion. Otsu is n utomtic threshold selection region sed segmenttion method. This pper works sed on the principle of inriztion of Otsu's method. In ddition, vrious enhncement schemes re used for enhncing n imge which includes gry scle mnipultion, filtering nd Histogrm Equliztion (HE). Histogrm equliztion is one of the well-known imge enhncement technique. It ecme populr technique for contrst enhncement ecuse this method is simple nd effective. In the ltter cse, preserving the input rightness of the imge is required to void the genertion of non-existing rtifcts in the output imge. Although these methods preserve the input rightness on the output imge with significnt contrst enhncement, they my produce imges with do not look s nturl s the input ones. The proposed work is divided into three stges. Firstly, Histogrm Equliztion is pplied to the low contrst input imge. Secondly, we re otining the imodl imge y pplying Otsu's inriztion, Thirdly, Otsu's thresholding technique is pplied to get the inry imge. The experimentl nlysis shows tht the results re etter thn the stte of the rt methods pplied. We hve shown the comprison results sujectively s well s ojectively. Sujective prmeters re visul qulity nd computtion time nd ojective prmeters re Pek signl to-noise rtio (PSNR), Men squred error (MSE nd Averge Informtion Content (AIC). Index Terms: Imge Enhncement, Otsu lgorithm, Segmenttion, Thresholding. I.Introduction Imge segmenttion is one of the most fundmentl nd difficult prolems in imge nlysis. Imge segmenttion is n importnt prt of imge processing. In computer vision, imge segmenttion is the process of prtitioning n imge into meningful regions or ojects. There re vrious pplictions of imge segmenttion like locte tumors or other pthologies, mesure tissue volume, computer-guided surgery, tretment plnning, study of ntomicl structure, locte ojects in stellite imges nd fingerprint recognition etc. Segmenttion sudivides n imge into its constituent region or oject. Imge segmenttion methods re ctegorized on the sis of two properties discontinuity nd similrity [].Bsed on this property imge segmenttion is ctegorized s Edged sed segmenttion nd region sed segmenttion. The segmenttion methods tht re sed on discontinuity property of pixels re considered s oundry or edges sed techniques. Edge sed A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 96

2 DOI: /ijecs/v6i8. segmenttion method ttempts to resolve imge segmenttion y detecting the edges or pixels etween different regions tht hve rpid trnsition in intensity nd re extrcted nd linked to form closed oject oundries. The result is inry imge. Bsed on theory there is two min edge sed segmenttion methods, gry histogrm sed nd grdient sed method []. Region sed segmenttion prtitions n imge into regions tht re similr ccording to set of predefined criteri. The region sed segmenttion is prtitioning of n imge into similr res of connected pixels. Ech of the pixels in region is similr with respect to some chrcteristic or computed property such s color, intensity nd/or texture. Imge enhncement plys significnt role in the field of Digitl imge processing Applictions for oth humn nd computer vision. It is minly used to enhnce the pprent visul qulity of informtion contined in n imge nd mkes it esier for visul interprettion, understnding s well s imge fetures process nd nlysis y computer vision system [3]. The ojective of this method is to mke n imge clerly recognize for specific ppliction. A Visul Imge is rich in informtion, Confucius sid, "A Picture is worth Thousnd words [4] The histogrm is one of the importnt fetures which re very relted to imge enhncement. The histogrm does not only gives us generl overview on some useful imge sttistics (e.g. mode, nd dynmic rnge of n imge), ut it lso cn e used to predict the ppernce nd intensity chrcteristic of n imge. If the histogrm is concentrted on the low side of the intensity scle, the imge is mostly drk imge. On the other hnd, if the histogrm is concentrted on the high side of the scle, the imge is mostly right imge. If the histogrm hs nrrow dynmic rnge, the imge usully is n imge with poor contrst [] Histogrm equliztion is one of the well-known imge enhncement technique. It ecme populr technique for contrst enhncement ecuse this method is simple nd effective. In the ltter cse, preserving the input rightness of the imge is required to void the genertion of nonexisting rtifcts in the output imge. Although these methods preserve the input rightness on the output imge with significnt contrst enhncement, they my produce imges with do not look s nturl s the input. The sic ide of HE method is to re-mp the gry levels of n imge. HE tends to introduce some nnoying rtifcts nd unnturl enhncement. Histogrms cn lso e tken of color imges - either individul histogrm of red, green nd lue chnnels cn e tken, or 3-D histogrm cn e produced, with the three xes representing the red, lue nd green chnnels, nd rightness t ech point representing the pixel count. The exct output from the opertion depends upon the implementtion, it my simply e picture of the required histogrm in suitle imge formt, or it my e dt file of some sort representing the histogrm sttistics. Histogrm equliztion technique is commonly employed for imge enhncement ecuse of its simplicity nd comprtively etter performnce on lmost ll types of imges. The opertion of HE is performed y rempping the gry levels of the imge sed on the proility distriution of the input gry levels. It flttens nd stretches the dynmic rnge of the imge's histogrm nd resulting in overll contrst enhncement. Contrst enhncement y histogrm equliztion is one such technique. A histogrm equlized imge enhnces the hrd to perceive detils of the originl imge. Nevertheless, the originl imge contins useful informtion. Gry level trnsformtions re the exmples of intensity trnsformtions. GHE lso usully cuses level sturtion (clipping) effects in smll ut visully importnt res [5]. This hppens ecuse GHE extremely A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 97

3 DOI: /ijecs/v6i8. pushes the intensities towrds the right (right) or the left (drk) side of the histogrm. If the input imge is dominted y drk intensity pixels, the histogrm is pushed to the right side, nd thus right sturtion effect will e clerly visile. On the other hnd, if the input is dominted y right intensity pixels, the output normlly suffers from drk sturtion effect. This sturtion effect, not only degrdes the ppernce of the imge ut lso leds to informtion loss [4] II.Literture work Fng et l. [5] proposed method to improve the enhncement result with imge fusion method with evlution on shrpness. Imge enhncement cn improve the perception of informtion C. Wng nd Z. Ye [6] proposed novel extension of histogrm equliztion, ctully histogrm specifiction, to overcome such drwck s HE (HISTOGRAM EQUALIZATION). To mximize the entropy is the essentil ide of HE to mke the histogrm s flt s possile Mry Kim nd Min Gyo Chung[7]Recursively seprted nd weighted histogrm equliztion for rightness preservtion nd contrst enhncement Chen Hee Ooi, Nichols Si Pik Kong, nd Hidi Irhim[8]Bi- Histogrm Equliztion with Plteu Limit for Digitl Imge Enhncement Pei-Chen Wu, Fn- Chieh Cheng, nd Yu-Kumg Chen[9]A Weighting Men-Seprted Su-Histogrm Equliztion for Contrst Enhncement S. D. Chen nd A. Rmli [0-] proposed generliztion of BBHE referred to s Recursive Men-Seprte Histogrm Equliztion (RMSHE) to provide not only etter ut lso sclle rightness preservtion Y. Wng, Q. Chen [] presented novel histogrm equliztion technique equl re dulistic su imge histogrm equliztion, is put forwrd in this pper. First, the imge is decomposed into two equl re su imges sed on its originl proility density function Y. T. Kim [3] proposed novel extension of histogrm equliztion to overcome such drwck of the histogrm equliztion D. Rjn nd S. Chudhuri [4] presented two new techniques of using dt fusion, sed on the modlity of the dt genertion process, to generte super resolved imge from sequence of low-resolution imge intensity dt III.Relted Study A. Histogrm Equliztion Histogrm equliztion is one of the well-known enhncement techniques. Histogrm equliztion technique is commonly employed for imge enhncement ecuse of its simplicity nd comprtively etter performnce on lmost ll types of imges. In histogrm equliztion, the dynmic rnge nd contrst of n imge re modified y ltering the imge such tht its intensity histogrm hs the desired shpe. This is chieved y using cumultive distriution function s the mpping function. The intensity levels re chnged such tht the peks of the histogrm re stretched nd the troughs re compressed. The histogrm of digitl imge with gry vlues r 0, r r L- is the discrete function: nk p( rk ) n Here nk denotes the numer of pixels with gry vlue r k, n denotes the numer of pixels in the given imge. p(r k ) represents the frction of the totl numer of pixels with gry vlue r k.. The glol ppernce of the imge cn e represented y the histogrm. Histogrm equliztion (HE) results re similr to contrst stretching ut offer the dvntge of full utomtion, since HE utomticlly determines trnsformtion function to produce new imge with uniform histogrm. k n k j sk T( rk ) p n j0 j0 r ( r ) Though this method is simple, it fils in myocrdil nucler imges since the gry vlues re physiclly fr prt from ech other in the j A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 98

4 DOI: /ijecs/v6i8. imge. Due to this reson, histogrm equliztion gives very poor result for myocrdil imges. B. Thresholding: Thresholding is n importnt technique in imge segmenttion pplictions. The sic ide of thresholding is to select n optiml gry-level threshold vlue for seprting ojects of interest in n imge from the ckground sed on their gry-level distriution. While humns cn esily differentile n oject from the complex ckground nd imge thresholding is difficult tsk to seprte them. The gry-level histogrm of n imge is usully considered s efficient tools for development of imge thresholding lgorithms. Thresholding cretes inry imges from grey-level ones y turning ll pixels elow some threshold to zero nd ll pixels out tht threshold to one. If g(x, y) is threshold version of f(x, y) t some glol threshold T, it cn e defined s [], G(x, y) = { ( ) Thresholding opertion is defined s: T=M [x, y,p(x,y), f(x,y)] In this eqution, T stnds for the threshold; f (x, y) is the gry vlue of point (x, y) nd p(x, y) denotes some locl property of the point such s the verge gry vlue of the neighorhood centered on point (x, y) Bsed on this, there re two types of thresholding methods. First one is Glol thresholding, When T depends only on f (x, y) (in other words, only on gry-level vlues) nd the vlue of T solely reltes to the chrcter of pixels, this thresholding technique is clled glol thresholding. The second one is Locl thresholding if threshold T depends on f (x, y) nd p(x, y), this thresholding is clled locl thresholding. This method divides n originl imge into severl su regions nd chooses vrious thresholds T for ech su region resonly [3]. Otsu method is type of glol thresholding in which it depends on only gry vlue of the imge. Otsu method ws proposed y Scholr Otsu in 979. Otsu method is glol thresholding selection method, which is widely used ecuse it is simple nd effective [4]. The Otsu method requires computing gry level histogrm efore running. However, ecuse of the onedimensionl which only consider the gry-level informtion, it does not give etter segmenttion result. So, for tht two dimensionl Otsu lgorithms ws proposed which works on oth gry-level threshold of ech pixel s well s its Sptil correltion informtion within the neighorhood. So Otsu lgorithm cn otin stisfctory segmenttion results when it is pplied to the noisy imges [5]. Mny techniques thus were proposed to reduce time spent on computtion nd still mintin resonle thresholding results. In [6], proposed fst recursive technique tht cn efficiently reduce computtionl time. Otsu's method ws one of the etter threshold selection methods for generl rel world imges with regrd to uniformity nd shpe mesures. However, Otsu's method uses n exhustive serch to evlute the criterion for mximizing the etween-clss vrince. As the numer of clsses of imge increses, Otsu's method tkes too much time to e prcticl for multilevel threshold selection [7]. C. OTSU S Method It works sed on the very simple ide tht minimizes the weighted within-clss vrince. This turns out to e represented sme s mximizing the etween-clss vrince. Once the histogrm is constructed for the given imge, it works directly on the gry level histogrm. For exmple, for gry level imge, there is proility of 56 numers i.e. P (, where I denotes the intensity vlue. Otsu lgorithm will consider some ssumptions such s the input imge is imodl, uniform illumintion etc. The weighted within-clss vrince is computed s: A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 99

5 DOI: /ijecs/v6i8. q q w The clss proilities q nd q re estimted s: q q t P( i I P( it The clss mens re computed s: re defined s pixels. Ech of these pixels cn e represented y triplet of RGB vlues in the cse of color imge, nd single vlue if the imge is in the gry scle. These vlues ssume ll vlues etween 0 nd 55. If you tke ll of the pixels of n imge nd count how mny of these hve vlue 0, how mny, how mny, nd so on up to 55, you get histogrm. The histogrm for the corresponding gry scle imge is shown in Figure. t ip( q i I ip( q it The individul clss vrinces re computed s: t [ i ( t)] i q P( I [ i ( t)] it q P( Here we hve to select the vlue which minimizes the rnge of vlues etween [,56]. We cn compute the results much fster with recursion reltion, reltion etween the within-clss vrince nd etween-clss vrince which is shown here: q [ q ][ ] w Where ( t ) is the within-clss vrince nd w q vrince. [ q( t)][ ( t) ( t)] is the etween-clss IV.Results & Discussion Otsu s inriztion technique is very importnt in the nlysis of imges, especilly in cses in which you wnt to pply threshold in the thresholding techniques in n efficient mnner. To understnd the concept we hve to know out the imodl imges. An imge is set of dots tht Figure :. gry scle imge. Histogrm of () As you cn see from the Figure, histogrm is nothing more thn wy to represent the distriution of the degree of color present in n imge. From the figure, for exmple, you cn view the presence of mximum in the middle. So, in this cse, the imge is monomodl. Insted of imge imodl, once represented in the form of histogrm, will present two seprte mximum etween them (modes). For exmple, this color imge tht I hve mde y dding it of ckground noise is imodl exmple. All the experimentl results re executed in OpenCV python. The first imge will e loded in gryscle nd then the histogrm of the imge will A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 300

6 DOI: /ijecs/v6i8. e generted. Displying it immeditely fter the imge vi mtplotli you cn see tht the imge chosen is perfectly imodl Consider the Figure. We cn oserve the imge presents two distinct modes mong them, dividing into two distinct prts the distriution of pixels in the imge. Here we hve tken n RGB color imge, first, we hve converted the RGB Color imge to Gryscle imge, nd then we hve constructed the imodl histogrm for the imge. Lter we hve pplied the Otsu's inriztion, the results re shown in Figure (d). Otsu s threshold on the imge. Results re shown in Figure 3. c d Figure :. Input imge. Gryscle imge of input imge c. Bimodl histogrm of (), d. Otsu s inriztion result As second exmple, we hve tken nother imge, with ckground noise nd pplied the c Figure 3: () Gry Scle Imge () Histogrm fter Threshold (c) Otsu s Thresholding However. The lgorithm fils if the input imge is hving poor contrst. If the input imge is poor imge, then Otsu s results re lso hving low in qulity. In this sense, we hve pplied Glol Histogrm Equliztion on the low contrst imge, then we hve pplied the Otsu s method on the resultnt imge. We hve oserved the good results when compred to the results without pplying the Histogrm Equliztion. The results re shown in Figure 4. The poor contrsted imge is shown in Figure 4. nd the result of Histogrm Equliztion is shown in Figure 4.. It is clerly shown tht the imge which is hving some ckground noise our method cn effectively pply the threshold nd given the etter result. Consider scenrio where the given is drk. Any imge my e drk ecuse of lighting conditions, illumintions sometimes the oject my itself drk. But the lgorithm gives the different results. A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 30

7 We hve worked in this cse of low contrst imges nd produced the good results. DOI: /ijecs/v6i8. Figure 5: () Histogrm of Figure 4(), () Histogrm of Figure 4() Here we cn oserve the sujective comprison of Otsu s results. Figure 6() shows the resultnt imge of Otsu inriztion without incresing the contrst in the imge. After pplying the Histogrm Equliztion the result is s shown in Figure 6(). Figure 4: () Poor contrst Imge, () Result of Glol Histogrm Equliztion of () The histogrms of Figure 4() nd 4() re shown in Figure 5() nd Figure 5() respectively. Figure 6: () Otsu s Result () Our result Qulity Mesurements: The comprison of vrious imge enhncement techniques sed on histogrm equliztion is A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 30

8 crried out in n ojective mnner for gry scle imges. Quntittive performnce mesures re very importnt in com-pring different imge enhncement lgorithms. Besides the visul results nd computtionl time, our evlution includes Entropy or Averge Informtion Contents (AIC). We hve computed the Men Squre Error nd Pek Signl Noise rtio nd Entropy for the imge to show our result ojectively. The Men Squre Error cn e computed s: MSE = ( ) ( ) The vlue of the MSE should e lwys minimum nd PSNR should e mximum for the etter results. Entropy or Averge Informtion Content is used to mesure the totl informtion in the imge. The higher vlue of Entropy indictes the richness of the detils in the output imge. A Higher vlue of the Entropy indictes tht more informtion is rought out from the imges nd it cn e computed s: AIC = - ( ) ( ) The computed vlues re MSE=43.0 nd PSNR=.96 nd entropy vlues efore nd fter pplying the method re.6 nd 0. respectively. V.Conclusion Imge segmenttion is the fundmentl pproch of digitl imge processing. Among ll the segmenttion methods, Otsu method is one of the most successful methods for imge thresholding ecuse of its simple clcultion. Otsu is n utomtic threshold selection region sed segmenttion method. We hve proposed n efficient lgorithm which consists of three steps. In the initil stge, contrst enhncement lgorithm is pplied to produce qulity imge. Next, we hve constructed imodl imge for the resultnt imge. Finlly, we hve pplied the Otsu's inriztion on the resultnt imge. The comprison results show the etter results thn the DOI: /ijecs/v6i8. existing method. In this pper, we hve considered the Histogrm Equliztion for the contrst enhncement. There re other vritions of Histogrm equliztion methods re lso pplied to get the etter results, which is considered s future work. References []. Rfel C. Gonzlez, Richrd E. Woods, Digitl Imge Processing, nd ed., Beijing: Pulishing House of Electronics Industry, 007. [] W. X. Kng, Q. Q. Yng, R. R. Ling, The Comprtive Reserch on Imge Segmenttion Algorithms, IEEE Conference on ETCS, pp , 009 [3]. Rfel C, Gonzlez, Richrd E, Woods & Steven L. Eddins, 004, Digitl Imge Processing Using MATLAB, Person Prentice Hll. [4]. S. Lu, 994, Glol imge enhncement using locl informtion, Electronics Letters, vol. 30, pp. 3. [5] Xioying Fng, Jingo Liu, Wenqun Gu, Yiwen Tng, A Method to Improve the Imge Enhncement Result sed on Imge Fusion, Interntionl Conference on Multimedi Technology, 0, pp [6] Y. Wng, Q. Chen, B. Zhng, Imge enhncement sed on equl re dulistic suimge histogrm equliztion method, IEEE Trns. on Consumer Electronics, vol. 45, no., pp , Fe [7] Mry Kim nd Min Gyo Chung, Recursively seprted nd weighted histogrm equliztion for rightness preservtion nd contrst enhncement, IEEE Trns. Consumer Electronics, vol. 54, no. 3, pp , August 008. [8] Chen Hee Ooi, Nichols Si Pik Kong, nd Hidi Irhim, Bi-Histogrm Equliztion with Plteu Limit for Digitl Imge Enhncement, IEEE Trns. Consumer Electronics, Vol. 55, No. 4, NOVEMBER 009. [9] Pei-Chen Wu, Fn-Chieh Cheng, nd Yu- Kumg Chen, A Weighting Men-Seprted Su- Histogrm Equliztion for Contrst A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 303

9 DOI: /ijecs/v6i8. Enhncement, IEEE Trns. Biomedicl Engineering nd Computer Science, 00. [0] S.-D. Chen, A. Rmli, Contrst enhncement using recursive men-seprte histogrm equliztion for sclle rightness preservtion, IEEE Trns. On Consumer Electronics, vol. 49, no. 4, pp , Nov.003. [] Y.-T. Kim, Contrst enhncement using rightness preserving i - histogrm equliztion, IEEE Trns. On Consumer electronics, vol. 43, no., pp. -8, Fe [] Iyd Jfr, nd Ho Ying, Multilevel Component-Bsed Histogrm Equliztion for Enhncing the Qulity of Gryscle Imges, IEEE EIT, pp , 007. [3] Nymlkhgv Sengee, nd Heung Kook Choi, Brightness preserving weight clustering histogrm equliztion, IEEE Trns. Consumer Electronics, vol. 54, no. 3, pp , August 008. [4] Gyu-Hee Prk, Hw-Hyun Cho, nd Myung-Ryul Choi, A contrst enhncement method using dynmic rnge seprte histogrm equliztion, IEEE Trns. Consumer Electronics, vol. 54, no. 4, pp , Novemer 008. [5] LIU Jin-zhung, Li Wen-qing, The Automtic threshold of gry level pictures vi Two-dimentionl Otsu Method, Act Automtic Sinic,993. [6] J. Gong, L. Li, nd W. Chen, Fst recursive lgorithms for two-dimensionl thresholding, Pttern Recognition,vol. 3, no. 3, pp , 998. [7] P. K. Shoo, S. Soltni, A. K. C. Wong, nd Y. Chen, A survey of thresholding techniques, Computer Vision Grphics Imge Processing, Vol. 4, 988, pp computing, computer vision nd Computer Grphics nd knowledge engineering. Mrs. C. Ush Rni is currently working s Assistnt Professor t the school of Computer Science nd Engineering in AITS, JNTUA. She received her mster s degree from JNTUA University in the yer of 00 nd Bchelor s degree from JNTUH University in the yer of 006. She hs 7 yers of teching experience nd tught vrious sujects in computer science strem nd orgnized vrious ntionl conferences nd workshops in the orgniztion. Her reserch interests re mchine lerning, Virtuliztion in the cloud, ig dt nlytics nd knowledge engineering. Mr. M.Sudhkr, currently working s Reserch Associte t VIT University, Chenni Cmpus. He received his mster s degree t the school of SCSE from JNTUA University in the yer 0. He received his Bchelor s degree in computer science nd engineering in 00. He hs pulished vrious ntionl nd interntionl journls. His reserch interests re imge nlytics, nd imge enhncement nd recommender systems. Authors Profile: Mr. A. Snkr Reddy is currently working s Assistnt Professor t the school of Computer Science nd Engineering in AITS, JNTUA. He received his mster s degree from JNTUA University in 04 nd Bchelor s degree from JNTUA in 007. He hs 4 yers of teching experience nd tught vrious sujects in computer science strem nd orgnized vrious ntionl conferences nd workshops in the orgniztion. His reserch interests re cloud A Snkr Reddy, IJECS Volume 6 Issue 8 August 07 Pge No Pge 304