Medical Image Fusion and Segmentation Using Coarse-To-Fine Level Set with Brovey Transform Fusion

Transcription

1 Research Journal of Appled Scences, Engneerng and Technology 4(19): , 2012 ISSN: Maxwell Scentfc Organzaton, 2012 Submtted: February 07, 2012 Accepted: March 15, 2012 Publshed: October 01, 2012 Medcal Image Fuson and Segmentaton Usng oarse-to-fne Level Set wth Brovey Transform Fuson P. Selvaran and V. Vathyanathan Sastra Unversty, Thanjavur, Inda Abstract: Ths study presents a fabrc level set method for contour extracton n medcal mages usng novel coarse-to-fne level set scheme. Medcal mage segmentaton s an atomc challenge for many researchers. The challenges are arsen due to the poor mage contrast and artfacts that result n dffuse organ/tssue boundares. Medcal mages are fused by usng Brovey transform fuson to ncrease the contrast of the mage. The dscrete wavelet transform s utlzed for extractng the medcal mages. oarse-to-fne level set scheme s used for perfect segmentaton. Extensve experments have been executed on Magnetc Resonance Images (MRI) and omputed Tomography Images (RI) to valdate the proposed algorthm. Keywords: Brovey transform fuson, coarse-to-fne level set, contour extracton, RI, dscrete wavelet transform, homogenety metrc, MRI INTRODUTION Image fuson s applcable n the medcal felds such as dagnoss and treatment. Fused mages can be performed wth multple mages whch s of the same certan type of the nformaton or by combnng wth multple nformaton such as magnetc resonance, computed tomography, postron emsson tomography and sngle photon emsson computed tomography. Images n radology and radaton oncology provde a dfferent purpose. For nstance, T mages are used to fnd out the contrast n tssue densty whle MRI mages are typcally used n dentfyng bran tumors. Brovey transform fuson s used n medcal mage fuson. Brovey fuson was ntroduced by Bob Brovey n the year Ths method s performed by dvdng each band nto all the layers. Each band s normalzed. It s then multpled wth panchromatc mage to acheve a fuse mage. Image segmentaton s a fundamental process n many mages, vdeo and computer vson applcatons. It s often used to partton an mage nto separate regons, whch deally correspond to dfferent real-world objects. It s a crtcal step towards content analyss and mage understandng. Medcal magng s the technque and process used to create mages of the human body for the scentfc purposes or medcal scence. Medcal mage segmentaton s a sgnfcant step n the mage analyss process. Sonka and Ftzpatck (2000) were developed the segmentaton n the medcal magery on T mages and MR mages. omputed Tomography (T) s also referred as computed axal tomography. It s based on medcal mage procedure whch uses X-rays to get cross segment mages Fg. 1: T mage Fg. 2: MR mage of the body. The T mage s depcted n Fg. 1. Magnetc Resonance Imagng (MRI) s a technque whch s used n radology to envsage detaled nteror structures. MRI uses the nuclear magnetc resonance property. The MR mage s depcted n Fg. 2. oarse-to-fne level set s a mathematcal technque whch s mplemented through the Euler Lagrange numercal equaton for mage segmentaton. The purpose of the coarse-to-fne level set s to fnd the complete 2D boundary of the salent objects n medcal mages. The advantage of coarse-to-fne level set s to segment the orrespondng Author: P. Selvaran, Sastra Unversty, Thanjavur, Inda 3623

2 Res. J. Appl. Sc. Eng. Technol., 4(19): , 2012 mages perfectly and also for avodng contour extracton problem. Our proposed dea s to effcently segment the mages whose mages are fused va Brovey transform. Brovey fuson s used to ncrease contrast of the mage by fusng T mage and MR mage. The fused mage s passed to the dscrete wavelet transform for extractng mages from a background. The dscrete wavelet transform s also used for calculatng ntensty dfference of the mage. Based on ntensty dfference, homogenety metrc s valdated. The homogenety metrc measures the varatons of the mages nsde and outsde contours. Based on the homogenety metrc, dscrmnatve ablty component s also computed. Weght dstrbuton rato s estmated based on the dscrmnatve ablty component. The result of homogenety metrc and the weght dstrbuton rato leads to a model whch s called as novel energy functon. Ths functon s passed to coarse-to-fne level set scheme n order to acheve perfect mage segmentaton. oarse to fne level set s mplemented through the Euler Lagrange equaton for solvng contour extracton problem. After segmentaton s over, segmented mage s evaluated by usng statstcal methods. LITERATURE REVIEW aselles (1993) and han and Vese (2001) has proposed an actve contour level set method n order to solve the contour extracton problem but they met wth many dffcultes. These methods use ntensty dfferences between mages and the background to extract contours. Mallad (1995) has proposed a shape modelng level set approach to solve contour extracton problem wth front propagaton but t also causes a falure. Kmmel (2003) and Gout et al. (2005) has proposed a geometrc level set method whch causes n less accuracy. Whtaker (2004) and Sethan (2005) has proposed the deformable surface level set method and fast ntegraton level set method n order to solve contour extracton problem but t also causes falure n generatng less accuracy. Law et al. (2008) has proposed a mult-resoluton stochastc level set method to solve contour extracton problem but they can move aganst aforementoned challenges. DeLus- Garc2acute (2011) has proposed a texture based segmentaton level set to solve contour extracton problem but t causes a lack of success n accuracy. Qzh et al. (2011) has proposed a mult scale level set method to solve contour extracton problem but t produces better accuracy n satellte mages. Ths level set s not applcable n medcal magery. Many several level set methods are appled for mage segmentaton whch results n less accuracy. To mprove the accuracy of the segmented mage, coarse-to-fne level set scheme and Brovey transform fuson s proposed. The man contrbutons are as follows. Fg. 3: Brovy transform output Fuson methods: Brovey transform s a wdely-used RGB color fuson. Ths transform s based on drect ntensty modulaton. The algorthm decomposes the phase space of the mult-spectral mage nto color and ntensty, whch essentally substtute the I component of mult-spectral mage wth hgh resoluton mage. It smplfes the mage transformaton coeffcent to reserve the mult-band mage nformaton and all the ntensty nformaton s transformed nto hgh resoluton panchromatc mage. Let R, G and B represent 3 mage bands dsplayed n red, green and blue. Let P represent the mage to be fused as the ntensty component of the color composte. The Brovey transform are defned as follows: R b = 3RP/R+G+B G b = 3GP/R+G+B B b = 3BP/R+G+B (1) The sum of the R, G and B bands are equvalent to the ntensty of hgh spatal resoluton mage. The equaton1 can be rewrtten as: R b = R*P/I G b = G*P/I B b = B*P/I (2) The operaton of the Brovey transform s smply done by multplyng each band wth the rato of the replacement mage over the ntensty of the correspondng color composte. If the mage P s hgher resoluton mage, then the Brovey fuson technque performs a good mprovement n spatal resoluton. The output of the fused mage s depcted n Fg. 3. Advantages: It s smlar to HIS fuson It s a smple method n fusng the mage alculaton s based on mathematcal arthmetc operatons Spatal resoluton of the fused mage s effcent Level set llustraton: The result of the fused mage s extracted from a background by usng dscrete wavelet transform. Starck et al. (2007) have ntroduced the dscrete wavelet transform/undecmated wavelet transform for extractng mages from a background. Here 3624

3 Table 1: Haar wavelet coeffcent H0 H1 G0 G Table 2: Daubeches wavelet coeffcent H0 H1 G0 G Res. J. Appl. Sc. Eng. Technol., 4(19): , 2012 we dscuss about how to segment the fused mage. It s performed by the followng procedure. Dscrete wavelet transform: Dscrete Wavelet Transform (DWT) s appled to dscrete nputs and produced dscrete outputs. Decmaton wavelet coeffcent s the ntrnsc property of the DWT. Wavelet transform computaton s faster and compacted n terms of the storage space. It has shft nvarant property. The shft nvarance of the wavelet coeffcent gves ncreased amount of the nformaton when compared wth the decmated wavelet transform. There are 3 knds of dscrete wavelet transform. They are: Daubeches wavelet Haar wavelet Hough wavelet Haar wavelet: Haar wavelet s smple wavelet. The requred memory needed n the haar wavelet s effcent. It takes less tme for dstngushng mages from a background. Input s represented by 2 n. The fnal output s n the dfference of 2 n -1. Haar wavelet s computed by low pass flter coeffcent and hgh pass flter coeffcent. It conssts of 2 dscrete values. One represents the runnng average values and the other represents the dfference or fluctuaton. It s used to ncrease the ntensty n the mage. Low pass flter s denoted as H0, H1. Hgh pass flter s represented as G0, G1. Based on these flters, haar wavelet coeffcent s valdated whch s shown n Table 1. The output of the haar wavelet s depcted n Fg. 4. Daubeches wavelet: Daubeches wavelets are a famly of orthogonal wavelets defnng a dscrete wavelet transform and characterzed by a maxmal number of vanshng moments. Daubeches wavelets are computed by scalng coeffcent and wavelet coeffcent respectvely. It takes more tme for dstngushng the mage from a background. Scalng wavelets are represented by H0, H1, H2, H3 and wavelet coeffcents are represented by G0, G1, G2 and G3, respectvely. Wavelet coeffcents are performed by usng scalng coeffcent whch s shown n Table 2. Lke haar, daubeches wavelet transforms are also computed the Fg. 4: Haar wavelet Fg. 5: Dabeches-output runnng averages and dfferences va scalar products wth scalng sgnals and wavelets. The output of the daubeches wavelet s depcted n Fg. 5. Among these, Haar wavelet takes less tme n decomposng the mages when comparng wth daubeches wavelet. Dscrete wavelet weghng approach: The purpose of the homogenety metrc s used to quantfy the varaton of the mages between nsde and outsde contours. Homogenety metrc s estmaton s done by the ntensty dfference of the daubeches wavelet. The homogenety metrc of d n regon S k (k = 0, 1, 2) s expressed by: E(d,S k ) = I(d (x,y)-d k ) 2 dxdy (3) Dscrmnatve ablty (d ) s computed by the followng expresson: η( d, c) E, 0+ ε0 = E, + E, + ε where d k s the mean value of d over regon S k. (4) Weght dstrbuton rato s estmaton s based on dscrmnatve ablty component: ξ( d, c) η( d, c) = 4 j η( d, c) j= 1 (5) The result of the dscrete wavelet weghng approach s novel energy functon model. Novel energy functon 3625

4 Res. J. Appl. Sc. Eng. Technol., 4(19): , 2012 model s a model whch s formed by combnng the homogenety metrc and the weght dstrbuton rato. T mage MR mage oarse-to-fne level set segmentaton: The purpose of ths scheme s the reducton of the resoluton level at a tme. It s appled n a large evoluton space whch reduces to small space. It contans 3 modules. They are: oarse scale model Fne scale model Statstcal model oarse scale model: oarse scale model s used for mnmzng the energy functon. Energy functon s obtaned by plottng the weghted components such as haar or daubeches wavelet, homogenety metrc, dscrmnatve ablty component and weght dstrbuton rato. It s expressed n terms of the equaton: Fuson Brovey transformaton Dscrete wavelet transform Segmentaton Novel energy functon oarse-to-fne level set Fg. 6: Block dagram proposed system F () c = µ Hφ dxdy + ξ ( d d 1) 2 Hφ dxdy N 4, c 2 ( ) ( ) + ξ d d 1 Hφ dxdy (6) c, 2 = 1 Ω 0 Fne scale model: ontour poston constrant s ntroduced for reducng the contour evoluton space to a small regon. It s measured by the space between boundares of the face mages. It s obtaned by the followng equaton: Rα ( x y) (,, ) dxy, = exp 2 γ α 1 (7) Statstcal model: Euler Lagrange s a suppostonal technque whch s used for reducng the energy set functon from a coarse space to a fne space. It s expressed by the followng term: ( ξ ( 1 ) ξ ( 2) ) φ φ = δφ ( ) Rα µ dv cd d cd d t φ,, = 1 PROPOSED METHODOLOGY (8) In ths secton, we dscussed about how coarse-to-fne level set works for segmentaton. Ths process s executed by the followng procedure. The block dagram s depcted n Fg. 6. The process s llustrated as follows. The process conssts of 2 concepts such as fuson and segmentaton. Brovey transform fuson s used for fusng T mage and MR mage. In BT, the mage s splt as color and ntensty component. Intensty component s replaced wth hgh resoluton panchromatc mage by usng Brovey transform formulae. Ths result s n fused mage. The fused mage s extracted from a background by usng dscrete wavelet transform. It s then passed to the dscrete wavelet Fg. 7: oarse-to-fne levelset output weghng approach whch forms a novel energy functon model. Fnally t s further passed to the coarse-to-fne level set for perfect segmentaton. The output of the coarse-to-fne level set s depcted n Fg. 7. DISUSSION AND ONLUSION The results are dscussed n ths secton 4. Fuson s mplemented n matlab 7.0 and Segmentaton n java 6 respectvely. The output of the coarse-to-fne level set s shown n Fg. 7. The qualty of the fused mage s evaluated by statstcal parameters such as mean square error, root mean square error, standard devaton, PSNR values, bas value, correlaton coeffcent whch s shown n Table 3. Medcal mage can be segmented wth 10 teratons. Iteratons can also be ncremented for further perfect segmentaton f necessary. Evaluaton of the segmentaton s performed by usng success score(s). Success score s calculated by S = (Number of pxels n both nput mage & segmented mage)/number of pxels n nput mage In general, the value of the success score ranges from 0-1. The segmented mage value s 1.0.e., the mage can be segmented perfectly. The above dscussons exhbted that the proposed method s sutable n solvng contour extracton by 3626

5 Res. J. Appl. Sc. Eng. Technol., 4(19): , 2012 Table 3: Shows the qualty of the fused mage Parameters values Mean nput Mean output Bas Bas relatve value Varance nput Varance output Varance dfference Varance relatve value Standard devaton Mean square error Root mean square error orrelaton coeffcent PSNR Standard nput Standard output segmentng the mages perfectly. It also exhbted that Brovey fuson s approprated to ncrease the ntensty of the mage. It s also showed that Haar wavelet s well suted for dstngushng mages from a background by ncreasng the ntensty dfference of the mage when compared wth the Daubeches wavelet. Ths paper s achevng a better performance by mprovng the accuracy of the mage. The success rate of the proposed approach s 100%. REFERENES aselles, V., F. atte, T. oll and F. Dbos, A geometrc model for actve contours. Numersche Mathematk, 66: han, T.F. and L.A. Vese, Actve contours wthout edges. IEEE Trans. Image Process, 10(2): DeLus-Garc2acute, R.A., R. Derche and. Alberola López, Texture and color segmentaton based on the combned use of the structure tensor and the mage components. Sgnal Process, 88(4): Gout,.,.L. Guyader and L. Vese, Segmentaton under geometrcal condtons usng geodesc actve contours and nterpolaton usng level set methods. Numer. Algorthms, pp: Kmmel, R., Fast Edge Integraton Geometrc Level Set Methods. Sprnger, pp: Law, Y.N., H.K. Lee and A.M. Yp, A multresoluton stochastc level set method for Mumford-Shah mage segmentaton. IEEE Trans. Image Process, 17(12): Mallad, R., J.A. Setan and B.. Vemur, Shape Modelng wth Front Propagaton: A level Set Approach. IEEE Trans. PAMI, 17: Qzh, X., L. Bo, H. Zhaofeng and M. hao, Mult scale contour extracton usng a level set method n optcal satellte mages. IEEE Geosc. Remote Sens. Lett. 8 (5). Sethan, J., A Level Set Methods and Fast Marchng Methods. ambrdge Unversty Press, ambrdge, UK. Sonka, M. and J.M. Ftzpatck, Handbook of Medcal Imagng: Medcal Image Processng and Analyss. SPIE Press, Bellngham, Wash. Vol. 2. Starck, J.L., J. Fadl and F. Murtagh, The undecmated wavelet decomposton and ts reconstructon. IEEE Trans. Image Process, 16(2): Whtaker, Modelng deformable Surfaces wth Level sets. IEEE omput. Graph., 24(5):