Image Segmentation Using Wavelet and watershed transform

Transcription

1 Imge Segmenttion Using Wvelet nd wtershed trnsform Atollh Hdddi, Mhmod R. Shei, Mohmmd J. Vldn Zoej, Ali mohmmdzdeh Fculty of Geodesy nd Geomtics Engineering, K. N. Toosi University of Technology, Vli_Asr Street, Mirdmd Cross, Tehrn, Irn, ABSTRACT In this pper imge segmenttion is performed y comining wvelet nd wtershed trnsform. If only wtershed lgorithm e used for segmenttion of imge, then we will hve over clusters in segmenttion. To solve this, we used n pproch. First we used the wvelet trnsformer to produce initil imges, then wtershed lgorithm ws pplied for segmenttion of the initil imge, then y using the inverse wvelet trnsform, the segmented imge ws projected up to higher resolution, in this wy, we could only cpture the lrge ojects. Since wvelet decomposition involves low-pss filter, the mount of the noise cn e decresed in imge which in turn could led to roust segmenttion. The results demonstrte tht comining wvelet nd wtershed trnsform cn help us to get the high ccurcy segmenttion, even in noisy imges nd SAR imges. The developed lgorithm ws pplied for segmenttion of color imges too. In this regrd, first, the imge ws trnsformed from RGB to other spces such s HSV, then the lgorithm ws pplied to segment ech chnnel seprtely nd then the est result for ech chnnel ws selected. Finlly, color mtching ws performed for etter presenttion. Results of proposed lgorithm in compre with segmented imge y the lgorithm in RGB spce is more ccurte nd furthermore proposed lgorithm cn e ensue n utomtic method for color imges nd multi nd imge segmenttion. KEY WORDS: segmenttion, wvelet, wtershed trnsform, region merging, SAR imge, speckle noise, color imge, color spce 1. INTRODUCTION N REMOTE sensing, imge segmenttion cn e used to detect lnd fields nd to improve pixel I clssifiction (Beulieu nd Touzi, 2004), nd tht is sic stge in imge processing ecuse the qulity of interprettion is depends on its result (Chrier et l). Tht is criticl stge to the success of lter high-level imge processing. Normlly, these high-level techniques re concerned with representing, interpreting, nd perhps enhncing the visul informtion present in n imge (Hvlicek, T. This is the first step in utomtic imge understnding process, such s feture extrction, clssifiction, oject detection nd recognition (Wng et l, 2004). By segmenttion process, imge divides into distinct regions. In fct ech region is equivlent with n oject (Jung,

2 2007). Such tht for ny region S i in n imge (I) S i I nd S S = fori j, nd S i = I (Beulieu nd Touzi 2004). There re mny pproches to imge segmenttion such s clssifictions, edges, or regions (Beulieu nd Touzi, 2004). Mthemticl morphology (MM) is powerful tool for imge segmenttion. Wtershed lgorithm is sed on MM nd is useful tool to imge segmenttion ut is very sensitive to noise nd leds to over segmenttion in imge. Mny pproches hve een developed to solve the over segmenttion in the imge (Jung, 2007). In this pper wtershed lgorithm ws used for imge segmenttion nd multi resolution technique y wvelet trnsformer ws pplied to reduce over segmenttion prolems cused y wtershed lgorithm. Using this method, we will hve decrese in the mount of noise nd lso the smll detils will e removed from imge nd only lrge ojects will remin. This ide hs mny dvntges in segmenttion of synthetic perture rdr (SAR) imges which re gretly complicted y the presence of coherent speckle, nd noisy imge. In this pper, we comined wvelet trnsform nd wtershed trnsform. To do this, first the wvelet trnsform ws used for denoising, which in turn leds to the production of four imges, pproximtion imge nd detil imges, then Soel opertor ws pplied for the estimtion of edges in this pproximtion imge., in this step dditionl edge is eliminted y threshold then initil segmenttion imge y pplied wtershed trnsform is otined. By using inverse wvelet trnsform, segmented imge with high resolution could e otined. It is possile to use the wvelet trnsform, two or more times, for producing the pproximtion imge until only the lrge ojects remin on the imge. To rech the high resolution in the projected segmented imge, the inverse wvelet could e repetedly used until we get resolution segmented imge tht is similr to the initil imge. A noisy imge nd SAR imge eside norml imge re selected to show the ility of this lgorithm for imge segmenttion. The results indicte tht this lgorithm hs high performnce. We developed this lgorithm to color imge too. To do this imge from RGB trnsformed to nother color spces, then the lgorithm is pplied. Result of proposed lgorithm in compre with segmented imge y the lgorithm in RGB spce is more ccurte. The pper is orgnized s follow. A rief description of the wvelet trnsform nd wtershed trnsform is presented in the sections 2 nd 3, respectively. Methodology is ddressed in the section 4. Section 5 includes SAR imge segmenttion. Developing this lgorithm to segment color imge is presented in section 6 nd the conclusion is presented in the finl section. i j 2. Wvelet trnsform The wvelet trnsform is importnt to provide compct description of signls (or imges) tht re limited in time (or sptil extent) nd it is very helpful in description of edge nd line tht re highly loclized. (Richrds nd Ji, 2006). 2-D wvelet decomposition is use for imges. This 2-D wvelet trnsform requires two wvelets, nmely, ψ 1 (x, nd ψ 2 (x,. At prticulr scle s we hve: i 1 i x y ψ s ( x, = ψ (, ) i = 1,2 (1) 2 s s s By pplying ech one f(x,, t scle s=2j we will hve component i i W j f x, = ( f W j )( x, i 1,2 (2) ( =

3 Then the originl signl f(x, cn e represented y the 2-D wvelet trnsform, in terms of the two dul wvelets ξ 1 (x, nd ξ 2 (x, 1 1 ( W j f j )( x, + ( W j f ξ j )( x, y ) f ( x, = ξ ) (3) And it is required scling function φ(x, for uild multistge representtion. Corresponding component t scle 2j is: S j f ( x, = ( f ϕ j )( x, (4) (Schrcnski et l, 2002). These wvelet mesure function vritions long different directions (Gonzlez nd Wood, 2002). We my interpret the component S f ( x, 2 j s smoothed version of f(x,, nd the components, for j=1,,j, s the imge detils lost y smoothing going from S 0 f ( x, to S f ( x, J (Schrcnski et l, 2002, Bshr et l, 2003). Indeed, y using wvelet on n imge for one level, four imges will e otined which correspond to the pproximtion nd detil imges. 3. Wtershed trnsform Wtersheds re one of the clssic regions in the field of topogrphy. A drop of the wter flling it flows down until it reches the utton of the region. In the field of imge processing, gryscle pictures re often considered s topogrphic reliefs, the numericl vlue (DN) of ech pixel is corresponding to the elevtion t this point (Vincent, Soille, 1991). This ide sys if we hve minim point, y flling wter, region nd the oundry cn e chieved. Wtershed use imge grdient to initil point nd region cn otin y region growing (Gonzlez nd Wood1, 2002). The min prolem of this lgorithm is over segmenttion, ecuse ll of edge nd noise would pper in the imge grdient, which mke the denoising process necessry. (Vincent, Soille, 1991) (see figure 1). In the imge nlysis, noise removl, without lurring the edge, is difficult. Typiclly, noise is chrcterized y high sptil frequencies in n imge. Furrier trnsform usully cn suppress the high-frequency component which is desirle effect, ut lso reduces the edge shrpness (Schrcnski et l, 2002). Therefore using Furrier trnsform for noise removl is not suitle. But wvelet trnsform provides good locliztion in oth sptil nd spectrl domins, nd low-pss filtering is inherent to this trnsform (Schrcnski et l, 2002). In this pper we use wvelet trnsform for noise removl nd lurring the imge for over segmenttion. Figure 1: ) originl imge. ) imge segmenttion y wtershed trnsform without ny processing

4 4. Methodology The digrm of lgorithm is presented in figure 2. In the first step wvelet trnsform to producing pproximtion nd detil imges is pplying, then y Soel msk, pproximtion imge grdient is otined nd dditionl edge is eliminted y threshold then wtershed trnsform for otining initil segmenttion is pplying nd segmented imge projected to high resolution y inverse wvelet using segmented imge in low resolution nd updted detil imges. Region merging is pplying in the lst. More detil s follow. Input imge Producing pproximtion nd Grdient pproximtion Initil segmenttion Updte detil imges Project segmented imge to high resolution Appointing lost pixel Resolution Segmented imge = initil Segmented imge Region merging Figure 2: implemented lgorithm for imge segmenttion Step 1: using wvelet trnsform The wvelet trnsform cn descrie n imge in different scle, nd due to existence of the lowpss filter in wvelet, noise mgnitude is reduced. Before using the wvelet, the wvelet function should e determined. To do this, we used the Hr method, ecuse it requires smll computtionl complexity (liner with respect to the size of the input imge). By pplying the wvelet on n imge, four imges will produce, tht the size of ech one is hlf of the originl imge; they re clled: HH, HL, LH, nd LL. The first nd second components correspond to horizontl nd verticl position respectively, nd the letter H nd L re representing the high nd low frequency respectively, (Jung, 2007). The reduction of resolution of the imge y wvelet is depending on the mount of noise. Figure 3 demonstrtes the output of this step for peppers in two norml nd noisy imges (NSR ). In this figure, the wvelet trnsform for the originl peppers imge nd noisy imge in the first nd second levels, respectively, re presented. Step 2: edge detection nd removl of the dditionl edge One of the most fundmentl segmenttion techniques is edge detection. There re mny methods for edge detection. We convolved the pproximtion imge y Soel msk nd clc mgnitude 1 Noise to signl power rtio

5 imge. For removing the remining noise, in the next step, we pplied threshold. Figure 4 shows the mgnitude of the originl nd noisy pproximtion imges. Figure 3: pproximtion of ) originl imge ) noisy imge Figure 4 grdient of ) originl imge ) noisy imge Step 3: using wtershed trnsform In the next step, y pplying the wtershed trnsform, initil segmenttion t the lowest resolution is otined. Figure 5 shows the output of this step for the originl nd noisy imges. Figure 5: wtershed in lowest resolution ) originl imge ) noisy imge Step 4: chieving high resolution segmented imge from low resolution The segmented imge hs low resolution with respect to the originl imge. By pplying the inverse wvelet trnsform nd using detil imges, higher resolution imge will e otined from the segmented imge. With repeting this step, the segmented imge nd originl imge will hve the sme resolution. It should e noticed tht efore using the inverse wvelet, only the informtion of the edge on the detils imge should e kept. See figure 6 for originl nd noisy imges. As shown in this figure, there re some pixels which re elong to no region, they re lost pixels. In the next step, we use n pproch for solving this prolem. Step 5: ppointing the lost pixel For ppointing the lost pixels, the intensity of the lost pixels ws compred to the eight non lost neighors pixels nd the intensity difference etween lost pixel nd non lost neighor s pixels re

6 computed. Lost pixel ppointed to the region tht hs minimum intensity difference. By repeting the steps 4 nd 5, the segmented imge will hve the sme resolution s the originl imge. Figure 6: segmented imge in high resolution, the lost pixels, re clerly seen. ) originl imge ) noisy imge Step 6: region merging In order to hve more reduction of the regions in the high resolution imge, region merging ws used. It mens tht, if the intensity of the two djcent regions ws smller thn threshold, they will e comined. It will reduce the numer of regions. Figure 8 shows the ppointing lost pixel nd region merging steps for the originl nd noisy imges. Figure 7: region oundry nd segmented imge in high resolution in originl nd noisy imges ) region oundry originl imge ) segmented originl imge c) region oundry noisy imge ) segmented noisy imge

7 Figure 8: ) segmented imge fter region merging in originl imge (threshold = 10) ) Segmented imge fter region merging in noisy imge (threshold = 10) 5. Results of the lgorithm on SAR imge This lgorithm ws tested on SAR imge too. We selected one of the cnl POLSAR imge, this dt is ville on 'Europen Spce Agency' wesite. Figure 9- shows this cnl POLSAR imge nd in figure 8- we cn see the segmented imge. As cn e seen in the figure 8-, due to the existence of the speckle noise in SAR imges, the trditionl methods hve less performnce in the segmenttion thn this lgorithm. So this lgorithm is more suitle for SAR imges segmenttion. Figure 9: ) SAR imge ) segmented SAR imge 6. GENERALIZATION OF THE ALGORITHM TO SEGMENT COLOR IMAGE We propose new lgorithm sed on mentioned lgorithm in section 4 for color imge segmenttion. In the first step we trnsform imge in RGB spce to YIQ spce (Prtt, 2001), HSV spce (Xio nd Ohy, 2007), HIS spce (Gonzlez nd Wood, 2002), L spce (Gonzlez nd Wood, 2002) nd YC C r spce (Prtt, 2001). In this cse, ech chnnel ws segmented y this lgorithm nd three optiml imges tht hd good result were extrcted. Color mtching ws performing in the finl for etter presenttion. We compred our method with spce RGB, we first, similr to conventionl lgorithm segment three chnnels (R, G nd B) seprtely nd then comine

8 them together then we used our lgorithm to compre with tht. Our result indicted tht proposed lgorithm ws etter thn previous lgorithm s show in figure 10---c. Figure 10-- demonstrtes the originl color imge nd segmented imge respectively otined from imge Q in YIQ spce, V in HIV spce nd C r in YC C r spce. Of course color mtching ws performed. c Figure 10: ) originl imge. ) proposed lgorithm. c) comine segmented chnnel R, G nd B In this pper optiml imge selected y visul nd three imges is used only. If optimiztion lgorithm is used to determine the est imge nd moreover ll imges prticipte in the segmented imge result, more relisticlly result cn e otined. 7. Conclusions In this work, we hve descried n pproch for imge segmenttion y comining the two wvelet nd wtershed trnsforms. Wtershed trnsform, is very sensitive to noise nd we will hve over segmenttion. To solve this prolem, we comined wtershed nd wvelet trnsformers for incresing result ccurcy. Resolution reduction y wvelet is depended on mount of noise in the imge nd lso the desired trget size. The otined results in this work showed tht this lgorithm exhiits high performnce in imge segmenttion even in the noisy nd SAR imges which contin speckle noise. In ddition, the developed lgorithm ws pplied on color imges for imge segmenttion. Result of the proposed lgorithm is etter thn comine segmenttion imge in three chnnels nd it cn e ensued s n utomtic method for color imge segmenttion. Future works will focus on comine the proposed lgorithm with optimiztion lgorithms for determining the est imge nd idelized it for ll kind imges such s flse color imges, eril nd stellite imges nd etc. Acknowledgements The uthors would like to express their thnks for M. Frhni for his vlule comments nd suggestions. REFERENCES Gonzlez, R., C., Woods, R., E., "Digitl Imge Processing", 2nd ed., Prentice Hll, New Jersey, USA, 2002, chp 6, 7. Richrds, J., A., Ji, X., Remote Sensing Digitl Imge Anlysis An Introduction", 4 th Ed., Springer, Berlin Heidelerg, Germny, 2006, chp 7. Prtt, W., K., Digitl Imge Processing, 3rd Ed., Wiley-Interscience, New York, 2001, chp 3

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