An Improved Image Segmentation Algorithm Based on the Otsu Method

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1 3th ACIS Internatonal Conference on Software Engneerng, Artfcal Intellgence, Networkng arallel/dstrbuted Computng An Improved Image Segmentaton Algorthm Based on the Otsu Method Mengxng Huang, enjao Yu, Dongha Zhu College of Informaton Scence & echnology Hanan Unversty Hakou,. R. Chna Abstract By analyzng the basc prncple of Otsu method ts applcaton n mage segmentaton, accordng to the dstrbuton characterstcs of the target background, an mproved threshold mage segmentaton algorthm based on the Otsu method s developed. By narrowng the selecton range of threshold searchng the mnmum varance rato, the mproved algorthm selects the optmal threshold. hrough the compared wth the Otsu method other methods, the results show that the new mproved algorthm has these advantages such as hgh segmentaton precson fast computaton speed. Keywords- Otsu method; mage segmentaton; optmal threshold; selecton range; mnmum varance rato I. INRODUCION Image segmentaton s one of the basc problems of the mage processng machne vson, ts key pont s: the mage s dvded nto a number of sets that do not mutual overlappng zones; these zones ether have meanng to currently msson or help to explan correspondence between them the actual object or some parts of object. Image segmentaton have a wde range of applcatons n practce, such as: ndustry automaton, product onlne detecton, manufacturng process control, remote sensng mage processng, bomedcal mage analyss, etc [][]. hreshold s a commonly used method that mproves the mage segmentaton effect obvously, meanwhle t s smpler easer to mplement. However, t fals when the dfference of the two wthn-class varances s large the result of Otsu method may be present twn peaks or more peaks [3]. By studyng the prncple of the Otsu method ts applcaton n mage segmentaton, an mproved threshold mage segmentaton algorthm based on the Otsu method s developed. By narrowng the selecton range of threshold searchng the mnmum varance rato, the mproved algorthm selects the optmal threshold. he results of the smulaton by dfferent mages were analyzed, studed, compared. he results show that the mproved Otsu algorthm has these advantages such as hgh segmentaton precson fast computaton speed. II. OSU MEHOD Otsu proposed a dynamc threshold selecton method n 979. hs method suggests maxmzng the weghted sum of between-class varances of foreground background pxels to establsh an optmum threshold [4]. e can partton the mage nto two classes at gray such that {,,, } { + +,}, where L s the total number of the gray levels of the mage. Let the number of pxels at gray level be n, N n be the total number of pxels n a gven mage. he probablty of occurrence of gray level s defned as n p p p. are normally N correspondng to the object of nterested the background, the probabltes of the two classes are p w p - as:. he means of the classes can be computed * p μ () L * p μ () + w So we can get the equvalent formula: σ ( ) w w( μw μw) (3) * he optmal threshold can be obtaned by maxmzng the between-class varance. * Arg max σ ( ) (4) < < he Otsu method s smple has a stable affecton, so t has been wdely appled n mage segmentaton n practce.it play an mportant role n the automatc selecton of threshold. However, we found that the Otsu method s very senstve about nose the sze of target. It s only effectve to the mage wth sngle peak varance after the experments to many knds of mages. hen the dfference of the two ntra-class varances s large, the threshold of the Otsu method tends to be closer to the class wth larger ntraclass class varance, whch means that more pxels of ths class wll be classfed nto the another class [5], so the segmentaton result needs to be mproved / $6. IEEE DOI.9/SND..6 35

2 III. HE ROOSED OSU MEHOD In order to overcome the above problems, ths paper presents an mproved Otsu method, the man steps of mproved Otsu method: Fgure. he man steps of mproved Otsu method A. Intal threshold segmentaton e can partton the mage nto two classes wth the mage mean grey value such that {,,, } { + +,L -}, where L s the total number of the gray levels of the mage. p * p n p p N here (5) B. Calculate lower threshold he means of can be computed as: * p w (6) here the s defned n (). C. Calculate hgh threshold Calculate the mean value of the class to get the hgh threshold * p + w here the w s defned n (). (7) D. Defne the scope of threshold It s very mportance to choose a reasonable threshold range for the optmzaton algorthm. Frst, we can exclude a large part of the gray values less tme s consumed greatly by narrowng the selecton range of threshold. Second, by removng the gray value whch s too low or too hgh, whch reduced the nose n the mage, so that reduced the false selecton rate of the optmal threshold. How to determne the threshold for the range of optons, n a number of papers have been dscussed. Generally speakng, snce the ntal threshold value s the mean of whole mage, the most dark areas of mage are belong to background, a small part of the lghter areas are the target, the fnal threshold must be greater than the frst threshold value. herefore, we can set the mean of class as the lower threshold, whch would elmnate a large part of low gray area, meanwhle wthout losng the potental optmal threshold. For the choce of the range of threshold, we consder the followng reasons: hen usng the ntal threshold to segment the orgnal mage, due to the excluson of a large part of low gray background pxels whch belong to, then the proporton of target areas wll ncreases n, the mean value ncreased, whch s hgher than the target area, so we set as hgh threshold. Fnally, the scope of threshold value s defned, so that we can search for optmal threshold n[, ]. E. Calculate nter-class varance ntra-class varance he varance of are defned as follow: σ (8) ( - μ ) * p L ( - w ) *p w (9) + w here, ]. he ntra-class can be computed as + w. he nter-class can be computed as: ( - ) w w F. Calculate the mnmum varance rato he mnmum varance rato s defned as: 36

3 σ λ () σ he nter-class varance σ ndcates the dfferent of class. here are more dfferences between class, the larger the σ wll be. he ntra-class varance means the dscrete degree of the class.he more concentrated the gray n the class, the lttler the σ wll be. Both of the nter-class varance the ntra-class varance are consdered when we choose the λ, so f we want to get the best segmentaton, ths method should make sure that λ as lttler as t can. G. Image segmentaton o dvde the 56 grey values nto two categores by optmal threshold value. Makng all of the grey value of pxels less than to be makng the gray value of pxels equal or greater than to 55. IV. HE RESULS OF HE SIMULAION EXERIMEN AND ANALYSIS In order to evaluate the performance of the proposed method, our algorthm has been tested usng mages Swan (48*3) mage Orange (56*56) n Fg., Fg.3 shows the hstogram of the mages. e can fnd that the dfference of the two ntra-class varances s large from the hstogram of the mages, so these mages are very sutable to test our algorthm. he basc nformaton of the mages showed n table I. Fgure. Orgnal mages: Swan, Orange ABLE I. HE BASIC INFORMAION OF HE IMAGES Swan Orange xel number Maxmum gray value Mnmum gray value Average gray values Lower threshold Hgh threshold 94 37

4 Fgure 3. he hstogram of the mage: Swan, Orange A. he effect of mage segmentaton he Otsu method s used to segment the mage n Fg. 4; however, t gves an ncorrect threshold value that fals to solate the contamnant. However, our method Zhong's method [6] whch s also an mage segmentaton based on the mproved Otsu algorthm successfully solated the contamnant n the mage. e can see from Fg. 5 Fg. 6 that the proposed method produces mages that are successfully dstngushed from the backgrounds. Fgure 5. Segmentaton results by method[6]: Swan, Orange Fgure 4. Segmentaton results by Otsu method: Swan, Orange Fgure 6. Segmentaton results by proposed method: Swan, Orange 38

5 B. Results analyss Compared the proposed method wth Otsu method the method [6] by repeat the test tmes. he test results showed n table II. ABLE II. Otsu method Method n [6] roposed method OIMAL HRESHOLD AND COMUING IME Swan Orange Optmal threshold Average tme /ms Optmal threshold Average tme /ms Optmal threshold Average tme /ms Followng conclusons from table II can be ganed. he threshold of Otsu method gettng optmal threshold s smaller than that the method [6] the proposed method get. From the hstogram of the mage Swan Orange, we can fnd that the sze of background target s very dfferent. he varance of background s bg the varance of target s small. Accordng to the prncple that when the dfference of the two varances s large, the threshold of the Otsu method tends to be closer to the class wth larger varance, whch means that threshold wll be small than the real threshold. hs means that the thresholds of the method n [6] the proposed method s more close to the real threshold than the threshold of the Otsu method. Meanwhle, the tme of Otsu method the proposed method consume s less than that the method [6] need. he reason s that the complexty of the proposed method the method [6] are ncreased, but the tme of the proposed method s reduced by narrowng the range of threshold selecton. V. CONCLUSIONS By analyzng the basc prncple of Otsu method ts applcaton n mage segmentaton, accordng to the dstrbuton characterstcs of the target background, an mproved threshold mage segmentaton algorthm based on the Otsu method s developed. By narrowng the selecton range of threshold searchng the mnmum varance rato, the mproved algorthm selects the optmal threshold. hrough the compared wth the Otsu method other methods, the results show that the new mproved algorthm s more close to the real threshold, so t s a more practcal effectve mage threshold segmentaton method ACKNOLEDGMEN hs work s supported by the Natonal Natural Scence Foundaton of Chna under Grant No. 767, the Socal Scence Fund roject of Mnstry of Educaton under Grant No. YJCZH49, the Key Scence echnology rogram of Hakou under Grant No. -67 the Scentfc Research Intaton Fund roject of Hanan Unversty under Grant No. kyqd4. REFERENCES [] He Jun, Ge Hong, ang Yu-feng, Survey on the methods of mage segmentaton research, Computer engneerng &scence, vol.3, no., 9. [] M. Sezgn B. Sankur. Survey over mage thresholdng technques quanttatve performance evaluaton, Journal of Electronc Imagng, pp.46-56, 3. [3]. K. Sahoo, S.Soltan, A.K. ong, Y. C. Chan, A survey of thresholdng technques, Computer Vson Graphcs, Image rocessng, vol.4, pp. 33-6, 998 [4] N. Otsu, A threshold selecton method from gray-level hstogram, IEEE ransactons on Systems Man Cybernet, SMC-8 pp. 6-66, 978. [5] Xu Xang-yang, Song En-mn, JIN Lang-ha, Characterstc Analyss of hreshold Based on Otsu Crteron, Acta Electronca Snca, vol.33, no.4, pp , 7. [6] Qu Zhong, Research On Image Segmentaton Based on the Improved Otsu Algorthm, Computer Scence, vol.36, no.5, pp , 9. [7] Jang Qn-yu, L ng, Sun Lan, Applcaton of Otsu method n moton detecton system, Journal o f Computer Applcatons, vol.3, no., pp. 6-6,. [8] Hu Chang-hua, Ne Zh-fe, Zhou Zh-je, Maxmum Classes Square Error B-hstogram Algorthm, System Smulaton echnology, vol.6, no.4, pp. 59-6,. [9] Chen S D, Ram L A R. Contrast enhancement usng recursve mean-separate hstogram equalzaton for scalable brghtness preservaton, IEEE ransactons on Consumer Electroncs, vol.49, no.4, pp. 3-39, 3. [] H. Lee, R. H. ark. Comments on an optmal threshold scheme for mage segmentaton, IEEE rans. Syst.Man Cybern, SMC-, pp.74-74, 99. [] J. Z. Lu,. Q. L, he Automatc thresholdng of gray-level pcture va two-dmensonal Otsu method, Acta Automatca S.9, pp.- 5,