Design and Implementation of 2D Spatial Filter for EEG and MRI Segmentation

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1 Design nd Implementtion of 2D Sptil Filter for EEG nd MRI Segmenttion Apurv Grg, Aknksh Gulti, Rhul Chuhn {Dept. of Electronics & Communiction Engineering}Grphic Er Hill University Dehrdun, Indi Astrct:- The Electroencephlogrphy (EEG) of rin field continues to e n ttrctive tool in clinicl prctice due to its rel time depiction of rin function.the im of this pper is to give review of digitl imge segmenttion technique.this pper study nd implements the different types of 2D sptil filter(weighted, smoothening,derivtive) for EEG segmenttion. Pper focuses on developing n utomted system to enhnce nd recover the corrupted EEG signl imges nd MRI imges with the help of 2D sptil filter nd it lso helps in erly nd ccurte dignosis of rin tumour. It ensures fst nd relile detection nd forml resolution of deformed imges y implementing noise ddition nd removl, edge detection, cropping, histogrm djustment, scle conversion s required y the imge. Keywords- EEG, 2D sptil filter, rin tumour ***** different from cncer. There re three common types of 1. INTRODUCTION tumour: 1) Benign; 2) Pre-mlignnt; 3) Mlignnt (cncer cn only e mlignnt) [8]. The imge segmenttion process cn e considered s one of the sic, yet very importnt, steps in digitl imge processing nd computer vision pplictions. Imge segmenttion is importnt prt in mny signl processing techniques nd its ppliction. The segmenttion procedure is to find the etter position of the shpe, points ccording to the ppernce informtion [4]. Segmenttion involves prtitioning n imge into set of homogeneous nd meningful regions so tht the pixels in ech prtitioned region possess n identicl set of properties or ttriutes. In medicl imges, segmenttion is minly done sed on the gry-level vlue of pixels, ecuse the mjority of medicl imges re grey-scle representtions [21]. For segmenttion of medicl imge relted to rin it s very importnt to understnd the terms EEG nd rin tumour. Electroencephlogrphy (EEG) is the recording of electricl ctivity of rin long the sclp. EEG mesures the voltge fluctutions resulting from ionic current flows within the neurons of the rin. EEG signl thus otined my crry noise long. This will e shown with the help of n exmple- Fig.1 EEG signl imge & filtered signl imge Figure 1 shows the imge of corrupted EEG signl nd the filtered signl, which hs removed noise from the originl EEG signl to improve its pictoril informtion. Now let us understnd wht is tumour nd wht is the preprocessing done to exmine the tumour. Tumour The word tumour is synonym to the word neoplsm which is formed y norml growth of cells within the rin or spinl cnl in centre [5] [6]. Tumour is something totlly 1) Benign tumour- A enign tumour is the one tht does not expnd in rupt wy, it doesn t ffect its neighouring helthy tissues nd lso does not expnd to non djcent tissues. Moles re the common exmple of enign tissues. 2) Pre-Mlignnt tumour- Pre-mlignnt is the precncerous stge, considered s disese, if not properly treted it my led to cncer. 3) Mlignnt tumour- Mlignnt tumour is type of tumour which grows worse with the pssge of time nd ultimtely results in the deth of person. Brin is kernel prt of our ody nd it hs very complex structure [18]. Due to complex structure of different tissues such s White Mtter (WM), Gry Mtter (GM), nd Cererospinl Fluid (CSF) in rin imges extrction of useful feture is fundmentl tsk [20]. Now dys, one of the min cuse for incresing mortlity mong children nd dults is the delyed detection of rin tumour. It is more difficult to detect tumour in erly stge nd lso ccurte mesurements re quite hrd ecuse of its shpe, size nd position of the tumour in the rin [16].Tumour my e emedded in regions of the rin tht re criticl to provide the ody s vitl functions, while they shed cells to invde other prts of the rin, forming more tumours tht re too smll to detect using the norml imging techniques [15]. It hs een concluded from the reserch of most of the developed countries tht numer of people suffering nd dying from rin tumours hs een incresed to 300 per yer during lst few decdes [8]. Erly nd ccurte dignosis of rin tumour is the key for implementing successful therpy tretment plnning. However the dignosis is very chllenging tsk due to the lrge vrince nd complexity of tumour chrcteriztions in imges, such s size, shpe, loction nd intensities [12]. According to Ntionl Brin Tumour Society, n estimted 688,000+ people re living with primry tumours of the rin nd centrl nervous system (CNS) in the initil sttes, 138,000 with mlignnt tumours nd 550,000 with nonmlignnt tumours. Aout 43% of rin nd CNS tumours 2666

2 occur in men nd out 57% occur in women. So efficient processing (AIP) [1]. DIP sends imge in the form of nd ccurte techniques re required for rin tumour codes which is more secure thn nlog imge processing detection [9]. The mjor prolem tht comes in the pth of which includes sending of imge in the form of nlog detection is noisy imges which led to wrong dignosis. signl nd secondly it conveys informtion with greter For right dignosis the imge cn e segmented y noise immunity s compre to A.I.P. pplying vrious 2d-sptil filters to get proper dignosis. To exmine humn rin development nd normlities of 2. PROPOSED METHODOLOGY rin cells tht is rin tumour or ny other disorder there re severl methodology used y rdiologists to exmine Red the imge the ptient physiclly y using mgnetic resonnce imging (MRI), Computed tomogrphy (CT) scn nd EEG. Bsiclly, for comprison, CT uses ionizing rdition while MRI uses strong mgnetic field to lign the nucler Convert the imge into doule mgnetiztion tht follows y chnges the lignment of the mgnetiztion y rdio frequencies tht cn e detected y Clcultion of totl numers of rows the scnner [13]. MRI is the stte of the rt medicl &columns imging technology which llows cross sectionl view of the ody with unprecedented tissue contrst [19]. MRI imges re essentil in clinicl dignosis of neurodegenertive nd psychitric disorders, tretment, Select type of filter evlution, nd surgicl plnning [14]. MRI imges shows the rin structures, tumor s size nd its loction [11].The imges thus otined using these techniques my crry noise which cn crete prolem in proper detection of the disorder. So the sptil filtering is used here for the noise If removl nd hence the proper detection of disorder cn e done. Let's tke n exmple of humn rin shown elow: i=1:5 Stop Medin filter: R= i=1 Z i Fig.2 Mgnetic Resonnce Imge (MRI) The imge of rin MRI shown here hve some ffected prt or hve some disorder, y exmining this imge we cnnot differentite etween effected prt of rin nd other prts of rin. Along with effected prt the veins of rin re not visile clerly in this imge. The imge so otined my result in wrong interception. To void this prolem imge enhncement is done y pplying vrious sptil filters which cn detect the edges, remove unwnted informtion with the help of lur nd shrpen the imge. The existing method of imge processing is sed on the threshold. At the threshold sed segmenttion the imge is considered s hving only two vlues either lck or white. But the itmp imge contins 0 to 255 gry scle vlues. So it ignores the tumour cells lso [10]. For tumour detection we need to detect the seed of tumour (i.e. center of the tumour cells) which my cuse intensity homogeneity prolems nd lso thresholded imge will not provide the cceptle result for ll the imges. In Digitl Imge Processing (DIP), we use computer lgorithm to perform imge processing, digitl imge processing hs severl dvntges over nlog imge g(x,y)= s= 2f x2 Low pss filter t= High pss filter: r s= Prewitt derivtive filter = f x f x 1 2f(x) Threshold f x, y = w(s, t) f(x + s, y + t) f x Stop t= w(s, t) = f x + 1 f(x) 0, T < 0 1, T

3 In the first prt of this pper uthors presented set of MATLAB sed pplictions useful for imge processing nd imge qulity ssessment. The imge processing nd imge qulity ssessment includes ppliction of low pss filter, high pss filter, Prewitt derivtive filter, medin filter nd threshold filter. We hve proposed segmenttion of the rin MRI imges for detection of tumour using different filter techniques. MRI scns of the humn rin forms the input imges for our system where the gry scle MRI input imges re given s the input. The pre-processing imge will convert the RGB input imge to gry scle [3]. Input MRI imge Preprocessing Sptil filtering To improve the required pictoril informtion of n imge of rin or EEG signl we will use five types of sptil filters which re s follows- 2.1 SMOOTHENING SPATIAL FILTER It is one of the simplest sptil filtering which cn e performed. This filtering cn e done simply y verging ll of the pixels in neighourhood round centrl vlue. The smoothening filter is lso known s verging filter nd low pss filter. A mjor use of verging filter is in the reduction of "irrelevnt" detil in n imge. By irrelevnt it mens pixel region tht re smll with respect to the size of filter msk. Another importnt ppliction of sptil verging is to eliminte smll ojects from n imge which re undesirle so tht intensity of smller ojects lends with the ckground nd lrger oject ecome "lo like" nd esy to detect. Fig.3 Block Digrm of proposed solution SPATIAL FILTERS Output imge (Monitor) Tumour detected Imge processing is of gret importnce nowdys s it involves chnging the nture of n imge in order to either improve its pictoril informtion for humn interprettion or Render it more suitle for utonomous mchine perception. It consists of four suunits [1]. Imge processing methods helps in the development of dignostic imging system with the help of filtering, restortion, segmenttion, reconstruction [17]. The first unit is intensity trnsformtions The second unit is sptil domin filtering which operte directly on the pixels of n imge. These techniques re efficient computtionlly nd require less processing resources to implement. The sptil domin processes cn e denoted y the expression- S(u,v)=X[f(u,v)] 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 Fig.5 R= i=1 Z i...1 * Fig.3 Simple verging filter e = 1/ 9 * / 9 * / 9 * / 9 * / 9 * / 9 * / 9 * / 9 *90+ 1 / 9 *85= Where f (u,v) is the input imge, s(u,v) is the output imge nd X is n opertor on f defined over neighourhood of (u,v). Origin (u,v) y x Sptil domin Fig.5 Sptil Domin Filter Msk Fig.6 Originl MRI imge Gry-scle imge Medin filter imge with less intensity Medin filter imge with more intensity The second type of verging filter is weighted verging sptil filter. In Weighted filtering the pixels re multiplied 2668

y different coefficients to give more importnce to some derivtives.[1] First derivtive for n imge cn e defined pixels.

The other pixels re inversely weighted s function of their distnce from the f centre of the filter. The sic strtegy ehind weighing the = f x + 1 f(x).

to reduce lurring in the smoothening It must e zero in res of constnt intensity. process. The generl implementtion for filtering n M*N It must e nonzero t the onset of n intensity step or rmp.

..2 Let s tke exmples to understnd low pss filter more clerly- Fig.

In this imge the veins nd edges re shrper s compred to the imge tken. Fig.

4 y different coefficients to give more importnce to some derivtives.[1] First derivtive for n imge cn e defined pixels. In this pixel closer to the centrl pixel re more s the given one-dimensionl function f(x)- importnt s compred to other pixels. The other pixels re inversely weighted s function of their distnce from the f centre of the filter. The sic strtegy ehind weighing the = f x + 1 f(x)...3 x centre point the highest nd then reducing the vlue of the For the ppliction of First derivtive definition it coefficients s function of incresing distnce from origin requiresis simply n ttempt to reduce lurring in the smoothening It must e zero in res of constnt intensity. process. The generl implementtion for filtering n M*N It must e nonzero t the onset of n intensity step or rmp. imge with weighted verging filter of size m*n (m & n It must e nonzero in constnt res. re odd) is given y the expression- g(x,y)= s= t= w(s, t) f(x + s, y + t) s= t= w(s, t)...2 Let s tke exmples to understnd low pss filter more clerly- Fig.8 Originl MRI imge Gry-scle imge (less shrp) High pss filter imge with less intensity High pss filter imge with more intensity Figure 8 shows the response of high pss first derivtive filter. In this imge the veins nd edges re shrper s compred to the imge tken. Fig.7 Originl MRI imge Gry-scle imge fter pssing it through Low pss filter Low pss filter imge with less intensity Low pss filter imge with more intensity. Figure 7 shows the response of rin which hs cvity. When the low pss filter is pplied to the imge it mkes the ckground lur nd mkes the cvity highlighted s shown in the figure. 2.2 SHARPENING SPATIAL FILTERS:-The principl ojective of shrpening is to highlight trnsitions in intensity. It is logicl to conclude tht shrpening cn e ccomplished y sptil differentition. The imge differentition ennces edges nd other discontinuities nd deemphsizes res with slowly vrying intensities. The derivtives of digitl function re defined in terms of differences. There re vrious wys to define these Second derivtive for n imge cn e defined with the help of given expression- 2f x2...4 = f x f x 1 2f(x) For the ppliction of second derivtive definition it requires- It must e zero long rmps of constnt slope. It must e nonzero t the onset nd end of n intensity step or rmp. It must e zero in constnt res. 2669

Fig.10 Originl MRI imge Gry-scle imge fter pssing it through Prewitt derivtive filter Prewitt derivtive filter imge with less intensity Prewitt derivtive filter imge with more intensity G x = 1 0 +1

9 Originl MRI imge Gry-scle imge fter pssing it through high pss filter(more shrp) High pss filter imge with less intensity High pss filter imge with more intensity Figure 9 shows the response of

Techniclly, it is discrete differentition opertor, computing n pproximtion of the grdient of the imge intensity function.

Mthemticlly, the opertor uses the two 3*3 kernels which re convolved with the originl imge to clculte pproximtion of the derivtives- one for horizontl chnges, nd one for verticl.

..5 Figure 10 shows the filtered imge of rin which only represents the edges of the imge. This imge cn e helpful in detection of cvity in rin esily. 2.

5 Fig.10 Originl MRI imge Gry-scle imge fter pssing it through Prewitt derivtive filter Prewitt derivtive filter imge with less intensity Prewitt derivtive filter imge with more intensity G x = * A Fig.9 Originl MRI imge Gry-scle imge fter pssing it through high pss filter(more shrp) High pss filter imge with less intensity High pss filter imge with more intensity Figure 9 shows the response of second derivtive high pss filter which represents the veins nd cvity shrper thn the first derivtive filter. 2.3 Prewitt derivtive filter: Prewitt derivtive is prticulrly n edge detection lgorithm. Techniclly, it is discrete differentition opertor, computing n pproximtion of the grdient of the imge intensity function. In simple terms, the opertor clcultes the grdient of the imge intensity t ech point, giving the direction of the lrgest possile increses from light to drk nd the rte of chnge in tht direction. Mthemticlly, the opertor uses the two 3*3 kernels which re convolved with the originl imge to clculte pproximtion of the derivtives- one for horizontl chnges, nd one for verticl. If we define A s the source imge, nd G x nd G y re two imges which t ech point contin the horizontl nd verticl pproximtions which re computed s- G x = * A...5 Figure 10 shows the filtered imge of rin which only represents the edges of the imge. This imge cn e helpful in detection of cvity in rin esily. 2.4 THRESHOLDING: It is the simplest method of imge segmenttion. It replces ech pixel in n imge with lck pixel if the imge intensity is less thn some fixed constnt vlue or white pixel if the imge intensity is greter thn tht constnt. It converts input gry-scle imge into inry imge formt [7]. A inry imge is logicl rry of 0 s (lck) nd 1 s (white). For conversion of gry scle imge to inry imge, toolox function im2w is used [2]. It scles the entire rnge of the input vlues to the rnge [0 1]. It mkes decision sed on locl pixel informtion nd is effective when the intensity levels of the oject fll squrely outside the rnge of levels in the ckground [21]. The thresholding concept hs een used in this lgorithm. The threshold concept works y choosing threshold vlue, T, utomticlly nd then extrct (or seprte) oject from ckground [9]. The threshold function of inry imge f(x,y) is defined s: f x, y = 0, T < 0 1, T

[8] Oelze, M.L, Zchry, J.F., O Brien, W.D., Jr., Differentition of tumor types in vivoy sctterer property estimtes nd prmetric imges using ultrsound cksctter,pge(s) :1014 1017, vol. 1, 5-8 O)ct. 2003.

6 [8] Oelze, M.L, Zchry, J.F., O Brien, W.D., Jr., Differentition of tumor types in vivoy sctterer property estimtes nd prmetric imges using ultrsound cksctter,pge(s) : , vol. 1, 5-8 O)ct [9] Prtih Shrm, Mnoj Diwkr, Sngm Choudhry in Appliction of Edge Detection for Brin Tumor Detection, Interntionl Journl of Computer Applictions (IJCA), vol. 58, No.16, Novemer [10] Atln Jose, S. Rvi, M. Smth in Brin Tumor Using Segmenttion K-Mens Algorithms nd Its Are Clcultion, Interntionl Journl of Innovtive Reserch in computer nd Communiction Engineering, vol. 2, Issue Fig. 11 Originl MRI imge Threshold imge 3, Mrch [11] Kimmi Verm, Aru Mehrotr, Vijyet Pndey, Shrdendu Figure 11 represents the imge in the form of two colour Singh in Imge Processing Techniques For The Enhncement Of Brin Tumor Ptterns, Interntionl levels tht is lck nd white. This imge is helpful in Journl Of Advnced Reserch In Electricl, Electronics nlysing the prts of rin. nd Instrumenttion Engineering, vol. 2 Issue 4, April CONCLUSION:-Segmenttion of rin imge is [12] V.P.Gldis Pushp Rthi nd Dr. S.Plni in Brin Tumor impertive in surgicl plnning nd tretment plnning in MRI Imge Clssifiction With Feture Selection And the field of medicine. In this pper uthor hve proposed Extrction Using Liner Discriminnt Anlysis, computer ided system for rin MR imge segmenttion Interntionl Jounl Of Informtion Sciences nd for detection of rin tumor. Here the different methods re Techniques (IJIST), vol. 2, No. 4, July clssified nd ech method hs its suitle ppliction [13] Ed-Edily Mohd. Azhri, Muhd. Mudzkkir Mohd. Htt, Zw Zw Htike nd Shoon Lei Win in Brin Tumor fields, nd reserchers should comine the ppliction Detection nd Locliztion In Mgnetic Resonnce ckground nd prcticl requirements to design proper Imging, Interntionl Journl of Informtion Technology lgorithms. Accurcy, complexity, efficiency nd Convergence nd Services (IJITCS), vol. 4, No.1, Ferury interctivity of segmenttion method should ll e the considered fctors. [14] Tuhin Uts Pul, Smir Kumr Bndhyopdhyy in Segmenttion of Brin Tumor from Brin MRI Imges REFERENCES- Reintroducing K-Mens with Advnced Dul Locliztion Method, Interntionl Journl of Engineering Reserch [1] Rfel C. Gonzlez (University of Tennessee), Richrd E. nd Appliction (IJERA), vol.2, Issue 3, My-June Woods (MedDt Intrctive) nd Steven L. Eddins (The [15] Monik Sinh, Khushoo Mthur in Improved Brin MthWorks, Inc.), in 'Digitl Imge Processing Using Detection with Ontology, Interntionl Journl of MATLAB' Second Edition, 2009 y Gtesmrk, LLC. Computtionl Engineering Reserch (IJCER), vol. 2, Issue [2] Alsdir McAndrew, in 'An Introduction To Digitl Imge no.2, Mr- April Processing with MATLAB, Notes for SCM2511 Imge [16] D. Anithdevi, K. 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