ANALYSIS OF ADAPTIF LOCAL REGION IMPLEMENTATION ON LOCAL THRESHOLDING METHOD
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1 Nusantara Journal of Computers and ts Applcatons ANALYSIS F ADAPTIF LCAL REGIN IMPLEMENTATIN N LCAL THRESHLDING METHD I Gust Agung Socrates Ad Guna 1), Hendra Maulana 2), Agus Zanal Arfn 3) and Dn Adn Navastara 4) 1,2,3,4 Informatcs Department, Faculty of Informaton Technology, Sepuluh Nopember Insttute of Technology (ITS), Surabaya e-mal: socrater.adguna@gmal.com 1), hendra.maulana23@yahoo.com 2), agusza@f.ts.ac.d 3), dn_navastara@f.ts.ac.d 4) ABSTRACT Thresholdng s a smple and effectve technque for mage segmentaton. Thresholdng technques can be grouped nto two categores, global thresholdng and local thresholdng. All local threshold method generally begns wth determnng thresholds n each pxel by checkng the area centered on the pxel, usng a box shape (x, y) whch s fxed by the sze of the neghborhood "b". If the neghborhood s very small, then the algorthm wll be senstve to nose and excessve segmentaton occurs. Whereas, f the sze of the neghborhood s very large then the algorthm wll apply resemble the global threshold method. In ths study, we propose a method of calculaton of Local Adaptve Regon, to determne the value of each pxel that s flexble neghborhoods, where each pxel has values dfferent neghborhoods based on the value of the standard devaton regon. Adaptve method on the local regon thresholdng conssts of several processes, namely: Image Enhancement, Adaptve Local Regon and thresholdng. Based on evaluaton of ME, mage result of threshold usng the Adaptve Local Regon method, gvng an average ME smallest value, that s 16.99% at Nblack method and 19.46% at Sauvola method. And on evaluaton of the RAE, mage result of threshold usng the Adaptve Local Regon method, gvng an average RAE smallest value, that s 15.26% at Nblack method and 25.58% at Sauvola method. In addton, the results of trals wth varous nose varance represent that the method of Adaptve Local Regon resstant to nose. Keywords: Thresholdng, Adaptve Local Regon, Nblack, Sauvola I. INTRDUCTIN I N dgtal mage processng, a method often used to detect objects n the grayscale mage s wth thresholdng method, where each pxel s then separated nto a background object [1]. An mage thresholdng results can be presented n the mage hstogram to determne the spread of the ntensty values of the pxels on an mage / specfc part of the mage Bnarzaton grayscale mages method can be grouped nto two broad categores: the methods of global thresholdng and local thresholdng method [2]. The global method s a method to fnd a sngle threshold value for the whole mage [3]. Ths global thresholdng method can be adapted to calculate the local boundary, where the value of dfferent thresholds used for each regon n the mage. Ths method s referred to as the Local thresholdng. Thresholdng locally made to areas n the mage. In ths case, the mage be broken down nto small parts, then thresholdng done locally. Local thresholdng method generally uses a threshold to the regon by analyzng a box-shaped area centered on the pxel (x, y) wth the value of neghborhood / neghborhood "b" [2]. Local thresholdng method wll work well f the value of a well-defned neghborhood. The amount of the neghborhoods should be adjusted to the sze of the object.the determnaton of the value of the neghborhood s done by repeated experments to obtan the optmal segmentaton results [4]. If the value s too great neghborhood, then the local thresholdng method wll work wth slow and small objects that have low contrast aganst the background value wll be dffcult to detect, especally f the object s close to the larger object wth hgh contrast value. Conversely, f the value of neghborhood s too small, there wll be an error n the determnaton of the borders of the object s large, resultng n large errors n dentfyng objects. Ths error also occurs n thnkng about the mage nose. Defcences n determnng the value of the neghborhood can be solved f the sze of the local regon's ar hub at pxel (x, y) has an area that s dynamc, accordng to the characterstcs of each area. In ths study, we proposed a method of calculaton of an adaptve local regon, where each pxel has values dfferent neghborhoods 1
2 Nusantara Journal of Computers and ts Applcatons based on the value of the standard devaton regon. Adaptve Methods n thresholdng local regon conssts of several processes, namely: Image Enhancement, Adaptve Local Regon and thresholdng. To determne the performance of the method, s used as a method of ME and RAE accuracy evaluaton system. II. PREVIUS RESEARCH Prevous research has been conducted related to local thresholdng method. Nblack method usng the average value and standard devaton of each pxel n an area wth a predetermned sze of grayscale mages. The amount of the neghborhoods must be small enough to be able to dentfy small objects and large enough to be able to elmnate the nose [5]. Dsadvantages of ths method s the hgh senstvty towards the sze of the neghborhood and the low resstance to nose n the bnary mage. Nblack method capable of beng upgraded by Sauvola wth remove nose n homogeneous regons wth an area larger than the sze of the neghborhood. Ths method proposed the hypothess that the object pxel has a value close to 0 and the background pxel has a value of 255. In areas wth hgh contrast values, Sauvola methods able to produce the same value wth Nblack method. The dfference n ths method wll be seen n the area wth a low contrast value, where the value of the local threshold s lower than the average value of the area so that t can be separated from the background. However, ths method has the same drawbacks wth Nblack method, the output from ths method greatly nfluenced n large regons of each pxel are fxed [6]. Fgure 1. Desgn System III. DESIGN SYSTEM The system desgn of the proposed method can be seen n Fgure 1. verall, the system n ths study s dvded nto several sectons, namely: pre-processng usng Blateral Flter Method, the calculaton of adaptve local regon, and the calculaton of the local thresholdng. A. Blateral Flter Blateral flter method s used at the stage of preprocessng. The purpose s to smooth the edges n the mage whle mantanng the sense of a lnear combnaton s not close to the value of the mage. The underlyng dea of the blateral flter s a flterng process n the range of an mage, whle the tradtonal flters do n ts doman [8]. Two pxels can be close to each other (occupyng the spatal locaton / room that was close), or they can be smlar to each other (havng a value close / smlar). Proxmty refers to the area around the doman, whle the smlarty n the area around the range. The tradtonal flter s a flterng doman and performs proxmty by comparson wth a coeffcent of the pxel values that match the dstance. Flter range s not lnear because of ts weght dependng on the ntensty of the mage / color. The combnaton of flter range and flter doman s a very nterestng combnaton that can be called a Blateral Flter. Because blateral flter assumes explctly on the dea wthn the doman and range of functons of the mage, they can apply for some of the functons defned second dstance. Blateral Flter can be defned as follows: 1 h ( x) k ( x) f ( x ) c( x, x) s( f ( x ), f ( x)) dx, (1) wth normalzaton k x) c( x, x) s( f ( x ), f ( x)) dx. (2) ( where f ( x ) an nput mage, h(x) s the resultng mage, c( x,x) s a measurement of geometrc proxmty between x and a neghborhood center closest pont x, s( f ( x ), f ( x)) s a photometrc measurement of smlarty between the pxels n the central neghborhood of x and ponts nearby x, and k(x) s the weght for each pxel. B. Adaptve Regon Method Varous prevous research has shown that the area of each pxel on the local thresholdng method should be large enough to recognze the borders of the object, but not too large to be able to elmnate pxels that are not relevant (lke nose). Therefore, the sze of the area relevant to the characterstcs throughout each pxel s requred to get optmum results local thresholdng. Ths study proposes a method of calculatng the value of the neghborhood s flexble by measurng the standard devaton σ (x, y) of each pxel regon centered at (x, y).each area has a crcular area wth mn- 2
3 Nusantara Journal of Computers and ts Applcatons mum radusr x,y, where the value of standard devaton regon (x,y) s greater than or equal to the standard devaton ( I ) of the mage.the radus of each pxel can be wrtten as Equaton 3. { ( ) }... (3) Ths method refers to the assumpton that f a gon has a standard devaton value greater than or equal to the standard devaton of an entre mage, then n the regon there s a structural nformaton suffcent to dvde a pxel nto an object or background. C. Adaptve Local Thresholdng A thresholdng T(x,y) s a threshold value to change the mage I(x,y) be a bnary mage B(x,y) as llustrated n Equaton 4. (4) In the Local Adaptve thresholdng method, the threshold value of each pxel s calculated based on local statstcs such as dstance, average, or devaton from the pxels of neghborhood [9]. ne method that s ncluded n the Local Adaptve thresholdng method s a method of Nblack and Sauvola.. 1) Nblack Method In ths method, the value of Y (x, y) at pxel (x, y) s calculated usng the adjacency (w x w), as n the followng equaton: (5) where s the average value of local and as the standard devaton of the pxel (x,y)n an area (wx w) and s a value bas. The standard value of s -0,2 and s 15. 2) Sauvola Method In Sauvola method, valuet(x,y)s calculated usng the average value of local and the standard devaton value n area (w x w) as n the followng equaton: [ ( )]... (6) Where R s the maxmum value of the standard devaton of the mage and a standard k value s 0.5 [1] [9]. IV. EXPERIMENT AND RESULT Regon Local Adaptve method was tested usng 10 mages that have varyng pxel ntensty values (Fgure 2) by comparng the results of mage segmentaton usng Local Adaptve Regon on Local Thresholdng method wth the mage of the local thresholdng segmentaton wth a value of neghborhood regulars. The great value of neghborhood stll used s the mnmum value of neghborhood that s 3 and the value used n several other studes [1] [5] [9], whch s 15. Groundtruth magery used, manually generated based on the orgnal mage as shown n Fgure 3. Fgure 4 and 5 ndcates the resultng mage thresholdng method Nblack and Sauvola use neghborhood fxed value of 15. Results of Adaptve Methods Local Regon mage shown n Fgure 6. In pcture 6, Regon Local Adaptve method pared wth Sauvola thresholdng method. To compare the qualty of the mage thresholdng, used quanttatve test usng the method of msclassfcaton error (ME) and Relatve Methods foreground area error (RAE) [1] [10]. Msclassfcaton Error (ME) s used for error rato correspondng background pxels are set as foreground and conversely. ME formula s defned as follows: B BT F FT ME 1, (8) B F where Bo s a background of the mage of the ground truth, Fo s a foreground of the mage of the ground truth, BT s a background of the mage are tested (the mage of the threshold) and FT s a tested foreground of the mage (the mage of the threshold). Whle Relatve foreground Area Error (RAE) s used to calculate the dscrepancy between the mage of the threshold wth an mage of the ground truth. RAE formula s defned as follows: RAE A A f A A T T A...(9) AT A f AT A A T Where Ao s an area of groundtruth foreground mage and A T s an mage of the foreground area threshold. Table 1 ndcates performance thresholdng based on the value of ME and RAE for each thresholdng algorthm whch s a comparson between the mage of the threshold wth the mage of hs ground truth. To test the robustness of the methods Adaptve Local Regon to nose, then do a test by addng nose to the mage based on the Gaussan dstrbuton. Each mage of the threshold to be tested characterzed by SNR (Sgnal to Nose Rato) s defned as follows: SNR x1 y1 N x N y 2 I x1 y1 N y N x ( x, y) ( I( x, y) I n ( x, y)) 2,...(10) wherei(x,y) andi n (x,y)s the level of gray to the pxels (x,y)on the reference mage and the mage to be tested.n x andn y s the columns and rows of mages. 3
4 Nusantara Journal of Computers and ts Applcatons V. DISCUSSIN Accordng to the evaluaton of ME, the mage of the threshold usng the Adaptve Local Regon, both at Nblack thresholdng method or Sauvola method, gvng an average ME value the smallest. Ths ndcates that the value of the background pxel error rato s defned as the foreground and conversely s the smallest. Meanwhle, accordng to RAE evaluaton, the mage of the threshold wth Regon Local Adaptve method also provdes a value RAE smallest n the second thresholdng method that ndcates that the sutablty of the object regon segmentaton between the mage of the threshold wth the groundtruth mage s greatest. Fgure 2. rgnal Image Fgure 3. Groundtruth Image 4
5 Nusantara Journal of Computers and ts Applcatons Fgure 4. Thresholdng Nblack Result (k = -0,2, w = 15) Fgure 5. Thresholdng Sauvola Result (k = 0,5, w = 15) Fgure 6. Adaptve Local Regon Method n Thresholdng NBlackMethod (k = 0,5) 5
6 Nusantara Journal of Computers and ts Applcatons Fgure 7. Adaptve Local Regon Method n Thresholdng Sauvola Method (k = 0,5) No. Image Nblack TABLE I CMPARISN RESULT THRESHLDING Sauvola Fxed Adaptve Fxed 3 15 Local Regon 3 15 Adaptve Local Regon ME(%) RAE(%) ME(%) RAE(%) ME(%) RAE(%) ME(%) RAE(%) ME(%) RAE(%) ME(%) RAE(%) Avg
7 ME (%) Nusantara Journal of Computers and ts Applcatons Nblack (w:3) Nblack (w:15) Nblack (adaptve) Sauvola(w:3) Sauvola(w:15) Sauvola(adaptve) Nose Varance Fgure 7. Graph Comparson of Results thresholdng the mage wth some nose varance Nose addton s done n stages wth the nose varance at 0:01, 0:02, 0:03, 0:04 and 0:05. Based on the value of ME and RAE, a decrease n the performance of the Adaptve Local Regon method average of 1%. Results of performance Adaptve Local Regon method also proved best at Nblack and Sauvola methods test to the addton of nose varance as shown n Fgure 7. The results of Nblack and Sauvola thresholdng method usng Adaptve Regon Local produce ME values far lower than that of both methods s the value of neghborhood fxed. VI. CNCLUSIN In ths paper, the calculaton method ndcated an adaptve local regon are sutable for use n local thresholdng method. The man key to ths method s the determnaton of the determnaton of the value of the neghborhood that s flexble for each pxel based on the standard devaton value of each pxel. Based on the experment, Nblack and Sauvola method whch uses the Local Adaptve Regon has a better performance than the Nblack and Sauvola method whch uses a fxed value of neghborhoods. Adaptve Local Regon Methods s able to provde ground truth mage correspondence well. Based on evaluaton of ME, mage result of threshold usng the Adaptve Local Regon method, gvng an average ME smallest value, that s 16.99% at Nblack method and 19.46% at Sauvola method. And on evaluaton of the RAE, mage result of threshold usng the Adaptve Local Regon method, gvng an average RAE smallest value, that s 15.26% at Nblack method and 25.58% at Sauvola method. Thresholdng performance evaluaton of the entre mage to be tested wth varous nose varance ndcates that the method of Adaptve Local Regon resstant to nose. For further development, need to do research to fnd methods of preprocessng better than Blateral Flter for mproved performance Adaptve Local Regon method. In addton, the applcaton of the method n certan cases also needed to determne the performance of the Adaptve Local Regon method. REFERENCES [1] Sezgn, Mehmet, Bulent Sankur Survey ver Image Thresholdng Technques and Quanttatve PerformanceEvaluaton. Journal of Electronc Imagng. [2] Sngh,. Imocha, et al Local Contrast and Mean Thresholdng n Image Bnarzaton. Internatonal Journal of Computer Applcatons [3] Frdous, Rukhsar, and Shaheen Parveen.2014.Local Thresholdng Technques n Image Bnarzaton. Internatonal Journal of Engneerng and Computer Scence. Vol 3. [4] Beneck, W. and Grabowsk, S., Mult-pass approach to adaptve thresholdng based mage segmentaton. In Proceedngs of the 8th Internatonal IEEE Conference CADSM. pp [5] W.Nblack (1986). An ntroducton to dgtal mage processng Prentce-Hall, Englewood Clffs,NJ. [6] Boangu, C.A., lteanu, A., Stefanescu, A., Rosner, D., Tapus, N. and Andreca, M.I., 2011, May. Local Thresholdng Algorthm Based on Varable Wndow Sze Statstcs. In Proceedngs of the 18th Internatonal Conference on Control Systems and Computer Scence (CSCS). Vol. 2, pp [7] C. Tomas, R. Manduch, Blateral Flterng for Gray and Color Images. IEEE Internatonal Conference on Computer Vson_98, 1998, [8] N. tsu A Threshold Selecton Method from Gray- Level Hstograms. Systems, Man and Cybernetcs, IEEE Transactons on, vol. 9, pp [9] Sngh, T.R., Roy, S., Sngh,.I., Snam, T. and Sngh, K., A new local adaptve thresholdng technque n bnarzaton. arxv preprnt arxv: Agus Zanal Arfn, Akra Asano, Image Segmentaton by Hstogram Thresholdng usng Herarchcal Cluster Analyss. Pattern Recognton Letters 27, 2006,
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