Masking based Segmentation of Diseased MRI Images

Transcription

1 Maskig based Segmetatio of Diseased MRI Images Aruava De Dept. of Electroics ad Commuicatio Egg. Dr. B.C. Roy Egg College Durgapur,Idia Raib Locha Das Dept. Of Electroics ad Commuicatio Egg, Dr. B.C. Roy Egg College Durgapur, Idia Aup Kumar Bhattacharee Dept. of Electroics ad Commuicatio Egg, Natioal Istitute of Techology, Durgapur,Idia Deepak Sharma, Shri Mata Vaisho Devi Uiversity, Katra, Jammu(J&K),Idia c.i Abstract We have devised a ew techique to segmet a diseased MRI image wherei the diseased part is segregated usig a maskig based thresholdig techique together with etropy maximizatio. The particle swarm optimizatio techique (PSO) is used to get the regio of iterest (ROI) of the MRI image. The mask used is a variable mask. The rectagular mask is grow usig a algorithm provided i the subsequet sectios usig similarity of eighbourhood pixels. Tests o various diseased MRI images show that small diseased obects are successfully extracted irrespective of the complexity of the backgroud ad differece i itesity levels ad class sizes. Previous works are based o bimodal images whereas our work is based o multimodal images. Keywords-Medical Resoace Imagig,Particle swarm optimizatio,withi-class variace,itesity cotrast,etropy I. INTRODUCTION Thresholdig is the most sigificat techique for image segmetatio. By selectig a suitable threshold we ca segregate the image ito obect ad backgroud. Thresholdig is a ecessary step i image aalysis applicatios. I a simple laguage thresholdig meas separatig the pixels i the image ito two groups amely obect ad backgroud. Oe group of pixels above a certai value called threshold ad aother group of pixels below the threshold. This type of image is a bi-modal image. It has a sigle obect ad backgroud. Aother type of images has may thresholds ad thus a umber of obects, such type of images are multimodal images. I a ideal case for a bi-modal image havig two classes the histogram cosists of two sharp peaks ad a valley i betwee the peaks. The peaks deote the obect ad the backgroud whereas the bottom of the valley is the threshold value for the image histogram. Multimodal images have a umber of peaks ad valleys i their image histogram thus multiple thresholds. I this paper we deal with multimodal image amely diseased MRI images of brai. The goal is to separate the diseased part of the MRI image from that of healthy part. May thresholdig approaches had bee defied ad studied ad the importat oes amog them are based o Valley detectio [2,3] ad criteria optimizatio techiques. Various criteria have bee proposed. The miimum error thresholdig method (MiError) assumes the ormal distributio for both obect ad backgroud [4,5]. The optimum threshold is achieved by optimizig a criterio fuctio related to the Bayes risk. Referece [6] estimated the parameters of ormal distributio correspodig to the obect ad backgroud from a trucated ormal distributio. The threshold is the determied by the Bayes decisio rule. Referece [7] is the first to propose thresholdig based o the maximum etropy priciple. Cross etropy is employed i threshold selectio by [8]. Referece [9] applied the fuzzy c- partitio to gray-level images ad selected the threshold by maximizig fuzzy etropy. Referece [10] itroduced a eergy criterio formulated by itesity-based class ucertaity ad regio homogeeity. The threshold is selected by miimizig the eergy. Referece [11] applied the idea of maximizig the betwee class variace ito histogram-based thresholdig. The method shows satisfactory results i various applicatios. However, it teds to split the larger part whe the sizes of obect ad backgroud are uequal [12]. Referece [6] poited out that the separatio of obect ad backgroud caot geerally be determied uiquely by the image histogram. Prior kowledge about the relatio ad properties of obect ad backgroud ca be helpful to improve the performace of thresholdig methods. Referece [13] took advatage of the kowledge about the rage of backgroud proportio to the ROI to cofie the rage of threshold selectio ad achieved reliable results i segmetig magetic resoace (MR) ad computed tomography (CT) images of the huma brai. Other prior kowledge may also be employed for proper selectio of threshold. Referece [1] demostrated that threshold ca be obtaied by optimizig the weighted sum of the withi-class variace ad the itesity cotrast. Theoretical bouds of the weight are derived for the uiformly distributed backgroud ad obect (Fig. 1), followed by the estimatio of the weight from prior kowledge about the rage of obect proportio ad the rage of the itesity cotrast. Referece [1] demostrated that usig their techique it is possible to segregate small obects from a large backgroud i a bimodal image. I our techique we segregate the image based o levels of itesity, because diseased portio of the MRI image will have a differet itesity value with that of a o diseased multimodal MRI image /10/$ IEEE

2 ad itesity cotrast ad its limitatio to segregate a diseased ad o diseased obect of MRI images. A ew thresholdig criterio ad the correspodig algorithm are the proposed to overcome this limitatio. Without losig geerality, we assume that the backgroud appears darker tha the obect. A. Previous Work Doe o Bi-ModalImage Figure 1. The histogram of uiformly distributed backgroud ad obect. We use etropy maximizatio to get the rage of gray level of diseased cells of MRI image. The rage is optimized usig PSO algorithm ad further fie tued usig the cocept of variable mask i which the mask is icremetally applied o the regio of iterest. Depedig o the similarity of the eighbourhood pixels the mask is icremeted. At the ed of the algorithm we fid that we have the regio with the diseased part segregated. Aalysis ad compariso are made agaist the [11], [1] ad [4]. Experimetal results show the superiority of the proposed method for segmetig diseased areas of MRI images. The paper is orgaized as follows. Next sectio describes the method of image acquisitio. Sectio 3 describes the previous work doe icludig the proposed criteria for segmetatio. Sectio 4 discusses the results of the proposed criteria. Sectio 5 is a comparative study of differet multimodal segmetatio algorithms with the proposed algorithm ad cocludig remarks are made i the last sectio. II. MRI DATA ACQUISITION AND ROI We have take a set of slices of huma brai of patiets for testig purposes. We have tested our method i a umber of patiets. The patiets iclude those who are curretly udergoig chemotherapy ad also those who are cured of the brai lesios. The lesios ca be aythig startig from tuberculosis to brai tumor. Ay differece with the ormal o diseased MRI image ca be obtaied usig this method. The images are viewed usig cetricity dicom viewer provided by GE Medical. Differet MRI views are as follows FSE Axial T2, AxFlairFast, O-Ax T1 SE S. All the examiatios were doe o the same 1.5-T MRI imagig device. Choice of regio of iterest (ROI) helps us reduce the time of the search ad thus makes the algorithm more efficiet. The ROI should iclude the obect i the presece of various variatios i the size ad positio of the obect. It is better if the ROI is as small as possible. III. THRESHOLDING ALGORITM We first aalyze briefly the limitatio of thresholdig based o the algorithm proposed by [1] usig withi class variace Figure 2. a) Origial Image b) Segmetatio usig Otsu method c)segmetatio usig Yu Qiao method Referece [11] presets a o-parametric ad usupervised method of automatic threshold selectio for segmetatio of image. A optimal threshold is selected by the discrimiat criteria which tries to maximize the separability of the resultat classes of the gray level i the image. The zeroth ad firstorder cumulative momets of the gray level histogram is utilized makig the procedure very simple. Fig. 2 is a sythetic image which cotais a white rectagular box i a black backgroud. There is a smooth variatio of itesities from the obect ad backgroud. The Otsu method misses the valley ad sets the optimum threshold somewhere ear the middle of the backgroud. Whereas Yu Qiao method was able to properly segmet the sythetic image. Referece [1] proposed a criteria that combies the withiclass variace ad the itesity cotrast, J( λ, T) = (1 λ) σw( T) λ mo( T) mb( T) (1) where m o (Τ ) ad m b (T ) are mea itesities of the obect ad backgroud, respectively. w (T) is the square root of withi-class variace, σ ( T) = P( T) σ ( T) + P ( T) σ ( T) (2) w b b o o where P b (T ) ad Po(T ) are probabilities of the backgroud ad obect, b 2 (T) ad o 2 (T) are correspodig variaces of the backgroud ad obect, respectively (see Ref. [11] for their calculatio). I this criterio, the withi-class variace measures the itesity homogeeity withi the obect ad backgroud while itesity cotrast captures the itesity differece betwee them. The parameter is a weight that balaces their cotributios. Whe = 0, the criterio degeerates to the withi-class variace. If = 1, thresholdig is determied oly by the itesity cotrast, which may yield the threshold at the largest itesity of the image. Therefore, the weight should be i the rage of [0, 1]. The optimum threshold T* is selected by miimizig the criterio.

3 J ( λ, T ) = mi J ( λ, T ) T (3) Eq. (3) actually tries to decrease the withi-class variace ad icrease the itesity cotrast simultaeously. I this way, the itesity cotrast becomes a explicit factor for determiig the optimum threshold. B. Previous Work doe o Multimodal Image Prior to the developmet of this work we have doe a survey of differet available Multimodal segmetatio algorithms. Creatig ad evaluatig differet segmetatio methods for diseased lesios i brai may be a difficult task altogether because the diseased lesios of brai are varied i ature. Referece [16] performed segmetatio of ischemic stroke lesios. Referece [17] proposed a adaptive mea shift framework for MRI brai segmetatio. Referece [16] classifies brai voxels ito oe of three mai tissue types amely gray matter, white matter ad Cerebro-spial fluid. It works well o 3-D sigle ad multimodal datasets, but the method does ot work well o the diseased lesios that we wat to detect i a sigle slice of a MRI. A comparative study with the above methods ad the proposed method is discussed i sectio 5. C. New Criteria of Segmetatio for Multimodal Images Oe of the basic disadvatages of the above system is that it caot segregate a image based o the itesity levels i case of Multimodal images (MRI images). The old method of withi class variace ad itesity cotrast oly segregates the image ito obect ad backgroud of a bimodal image but fail for multimodal images. I real time MRI images the ability to distiguish a diseased cell from a o diseased oe is particularly importat for diagostic purposes. We itroduce the cocept of low pass filterig by usig a variable sized mask to distiguish betwee diseased ad o diseased portios of a MRI image. MRI image has a multimodal histogram (Fig. 3) i.e it may have umber of valleys istead of a sigle valley i case of bimodal histogram. Thus the [1] method fails i the segregatio of the image. We use the Etropy maximizatio techique ad the correspodig optimizatio of the fuctio H (Shao Etropy) for the threshold gray level rage at which the diseased cells differ from that of o diseased cells. Now the threshold value of segregatio is tha modified to get a exact value at which the diseased ad o-diseased cells are completely segregated. 1) Etropy Maximizatio ad Expert Kowledge. First we compute the ormalized histogram h() for a gray image f(x,y). P = h( ) = f / N, = 0,1,2, (4) P =h() 0 t 1 t 2 t 3 Figure 3. Multimodal Histogram Where f is the observed frequecy of gray level (or f is the umber of pixel that havig gray level ) ad N is the total umber of pixels i the picture. For multimodal image we wat to divide the total image ito (k+1) umber of homogeeous zoes ad for that we cosider the threshold gray levels at t 1, t 2, t 3,...t k. Shao Etropy is defied as t1 t l 1 2 l 2... k l k = 0 = t1 + 1 = t + 1 (5) H = P P P P P P Where t1 1 / = 0 P = P P for 0 t 1 t2 2 / = t1 + 1 P = P P for t 1 < t P = P / P for t k < 255 k = tk + 1 We have obtaied the fuctio H for the threshold gray level t 1 to t k usig PSO (Particle Swarm optimizatio) algorithm. Now we apply this expert kowledge that the gray level of diseased zoe of a MRI image vary from rage say T x to Ty. Usig this expert kowledge, we apply our cocept of variable low pass filter mask after optimizig the threshold rage usig PSO which is explaied i subsequet sectio. 2) Optimizatio usig PSO algorithm. We optimize the basis fuctios obtaied usig Etropy maximizatio techique usig the cocept of PSO algorithm. PSO algorithm as applied to our techique: Where To maximize fuctio f ( X ) with () l X ad X X X () l ( u) ( u) X deote the lower ad upper bouds o

4 X. Get the ormalized Histogram of diseased MRI image Get the regio of iterest usig the threshold value Startig with 3 3 mask search for similarities i regio growig maer i vertical/ horizotal directios Figure 4. Flow diagram of the proposed method PSO works as follows: 1) Assume the size of the swarm (umber of particles) is N. () l X 2) Geerate the iitial positio of X i the rage ( u) ad X radomly as X1, X2... X N. 3) Velocity is assiged for each particle as V, V... V N. 1 2 Etropy maximizatio to defie a fuctio to get the expert kowledge of the probable threshold gray level rage of diseased cells of MRI image Optimize probable threshold gray level usig PSO algorithm Get the differet regios of diseased cells of the MRI image which ca be used for diagosis 4) Evaluate obective fuctio values correspodig to the particles. Iitially all the particle velocities are assumed to be zero ad set the iteratio umber as i=1. 5) I i th iteratio fid the followig two parameters. Calculate the historical best value of X () i called P best,. Also fid G best, the highest value of obective fuctio ecoutered i all the previous iteratios by ay of the N particles. 6) Update the velocity as follows V( i) = θv( i 1) + c11 r[ Pbest, X ( i 1) (6) + cr 2 2[ Gbest X ( i 1)]; = 1, 2... N. 7) Update the positio of the particle as X ( i) = X ( i 1) + V ( i); = 1, 2... N (7) 8) Check the covergece of the curret solutio. If the covergece criteria is ot satisfied the repeat steps 5 6,7. The PSO algorithm is used for optimizig the iitial value of threshold to be used for segmetig the MRI image usig variable mask. I the case of Fig. 5, we take te particles for each of three dimesioal spaces take, where each dimesio of space represets a threshold value. Usig PSO we get three threshold values 20, 100, 157. Usig expert kowledge we observe that diseased cells of MRI image lie above the threshold value of 157 usig θ =0.9 to 0.4, c 1 =1 ad c 2 =2. For Fig. 10 we obtai the threshold values of 20, 46, 175. We choose the threshold value of 175 for the purpose of detectio of the diseased lesios. Usig the threshold value we get the regio of iterest which cotais all diseased cells alog with small umber of o diseased cells, the results of the process is show i Fig. 8 ad also Fig. 12. The variable mask is applied i a regio growig maer o the ROI to get the fial segmeted MRI image as show i Fig. 9 ad Fig ) Cocept of variable mask. We start with a 3 3 mask. All the pixel positios havig a value more tha the threshold value obtaied usig Etropy maximizatio forms the ROI. The mask is moved over the ROI, if the eighborhood pixels display a similar value we icrease the size of the mask. The mask is grow i all the four directios depedig o the similarities of the pixel values. If the eighborhood pixels have differet value the we agai start afresh i the ew regio of the ROI. After executio of this procedure we get a segregated image cotaiig the diseased cells of the MRI image. Fig. 4 shows the flow diagram of the proposed techique. IV. RESULTS AND DISCUSSIONS The PSO algorithm obtais a threshold value which helps us to obtai the regio of iterest for Fig. 5 ad Fig.10.The regio of iterest cotais all the diseased cells ad it may cotai a portio of o diseased cells of the MRI image. The results of the PSO algorithm are show i Fig. 8 ad Fig. 12. The parameters of PSO algorithm is already discussed i the sectio explaiig optimizatio usig PSO algorithm. The variable mask is applied o the regio of iterest to obtai the fial segmeted MRI image as i Fig. 9 ad Fig. 13. Referece [11] applied the idea of maximizig the betwee class variace ito histogram-based thresholdig to bimodal images. The method shows satisfactory results i various applicatios. However, it teds to split the larger part whe the sizes of obect ad backgroud are uequal. Hece [1] improved upo the [11] method with satisfactory results but [1] ad [11] both do ot work for multimodal images such as that

5 of diseased huma brai MRI image. Referece [1] failed to segmet the diseased MRI huma brai as show i the Fig. 7. The compariso shows that the proposed method is useful for segmetig multimodal diseased MRI image. The diseased cells are correctly segregated usig the proposed method as show i the experimetal results Fig. 9 ad Fig. 13. Figure 9. Segmetatio usig the proposed method. Figure 5. Diseased MRI image. Figure 10. Diseased MRI image Figure 6. Histogram of Figure 5 Figure 11. Histogram of Figure 10 Figure 7. Segmetatio usig Yu Qiao method. Figure 12. Optimised MRI image usig proposed method Figure 8. Optimised MRI image usig proposed method

6 Figure 13. Segmetatio usig proposed method VI. CONCLUSION AND FUTURE WORKS The previous methods worked o bimodal images with a histogram havig two peaks but they failed whe applied to multimodal images with a histogram havig multiple peaks. The proposed method works o multimodal images as show i the results. The proposed method is very useful for diagosis of diseased MRI images. Differet optimizatio algorithm may be used ad a comparative study of the results ca be made for cacer diagosis i broad rage of huma orgas amely brai, lymph odes etc. V. COMPARATIVE STUDY The comparisos were doe with differet existig bimodal ad multimodal segmetatio algorithms. The compariso with the bimodal segmetatio algorithms have bee already discussed i the previous sectios. The proposed method was used o diseased MRI image as i Fig.5 ad the results were obtaied as depicted i Fig. 8 ad Fig. 9. The same image was used as a iput for the algorithm proposed by Yu Qiao but the results were ot satisfactory as depicted i Fig. 7. Before performig the segmetatio usig [16], the skull is removed so that oly the brai tissues remai. Here Axial T2 sequece Fig. 14 (same as Fig. 5) was used to brig out the usefuless of the multimodal approach for sigle sequeces oly. Here we have cosidered for a sigle sessio. We cosider K=4 correspodig to four classes white matter, gray matter, Cerebro-Spial fluid ad the lesio. The results depicted i Fig.15 are ot satisfactory compared to Fig.9. Figure 14. Axial T2 with Brai cells oly Figure 15: Segetatio of 14 usig [16] REFERENCES [1] Y. Qiao, Q. Hu, G. Qia, S. Luo, ad W. L. Nowiski, Thresholdig based o variace ad itesity cotrast, Patter Recogitio, vol. 40, pp , [2] J.M.S. Prewitt, ad M.L. Medelsoh, The aalysis of cell images, A. N. Y. Acad. Sci. vol. 128, o. 3, pp , [3] J.S. Weszka, ad A. Rosefeld, Histogram modificatio for threshold selectio, IEEE Trasactios o Systems, Ma ad Cyberetics, vol. 9, o. 1, pp , [4] J. Kittler, ad J. Illigworth, Miimum error thresholdig, Patter Recogitio, vol. 19, o. 1, pp , [5] S. Cho, R. Haralick ad S. Yi, Improvemet of Kittler ad Illigworth s miimum error thresholdig, Patter Recogitio, vol. 22,o.5,pp ,1989. [6] J.S. Lee ad M.C.K. Yag, Threshold selectio usig estimates from trucated ormal distributio, IEEE Tras. Syst. Ma Cyber.,vol. 19,o. 2,pp ,1989. [7] T. Pu, Etropic thresholdig: a ew approach, CVGIP: Graphical Models Image Process.,vol. 16,pp ,1981. [8] C.H. Li ad C.K. Lee, Miimum cross etropy thresholdig, Patter Recogitio, vol. 26,o. 4,pp ,1993. [9] H.D. Cheg, J.R. Che ad J.G. Li, Threshold selectio based o fuzzy c-partitio etropy approach, Patter Recogitio,vol. 31,o. 7, pp ,1998. [10] P.K. Saha ad J.K. Udupa, Optimum image thresholdig via class ucertaity ad regio homogeeity, IEEE Tras. Patter Aal. Mach. Itell.,vol. 23, o.7,pp , [11] N. Otsu, A thresholdig selectio method from gray-level histograms, IEEE Tras. Syst. Ma Cyber,vol. 9,o. 1,pp ,1979. [12] J. Kittler ad J. Illigworth, O threshold selectio usig clusterig criteria, IEEE Tras. Syst. Ma Cyber.,vol. 15, o. 5, pp ,1985. [13] Q. Hu,Z. Hou ad W.L. Nowiski, Supervised rage-costraied thresholdig, IEEE Tras. Image Process,vol. 15,o. 1,pp ,2006. [14] C.A. Glasbey, A aalysis of histogram-based thresholdig algorithms, CVGIP: Graphical Models Image Process, vol. 55, o.6, pp ,1993. [15] S. S. Rao, Egieerig optimizatio :Theory ad practice, pp , Fourth editio, Joh Wiley ad Sos, [16] Y.Kabir, M.Doat, B.Scherrer, F.Forbes,C.Garbay, Multimodal MRI Segmetatatio of Ischemic Stroke lesios, Proceedigs of the 29 th Aual Iteratioal Coferece of the IEEE EMBS, Cite Iteratioale,Lyo Frace,August 23-26, [17] Araldo Mayer ad Hayit Greespa, A Adaptive Mea-Shift Framework for MRI Brai Segmetatio, IEEE Trasactios o Medical Imagig, vol. 28,No. 8, pp , August [18] MC.Clark et al.., Automatic tumor-segmetatio usig Kowledgebase techiques,ieee Trasactios o Medical Imagig, pp 117, ,1998

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