Quadratic Program Optimization using Support Vector Machine for CT Brain Image Classification

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1 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 4, o, July ISS (Onlne): Quadratc Program Optmzaton usng Support Vector Machne for CT Bran Image Classfcaton J Umamaheswar and Dr.G.Radhaman Research Scholar, Department of Computer Scence, Dr. G.R.D. College of Scence, Combatore, Tamlnadu, Inda Drector, Department of Computer Scence, Dr. G.R.D. College of Scence, Combatore, Tamlnadu, Inda Abstract In ths paper, an effcent Computer Tomography (CT) mage classfcaton usng Support Vector Machne (SVM) wth optmzed quadratc programmng methodology s proposed. Due to manual nterpretaton of bran mages based on vsual examnaton by radologst/physcan that cause ncorrect dagnoss, when a large number of CT mages are analyzed. To avod the human error, an automated optmzed classfcaton system s proposed for abnormal CT mage dentfcaton. Ths s an automated system for content based mage retreval wth better classfer accuracy and predcton tme. SVM classfer can accurately tran up the data s as normal and abnormal brans nterpreted manually by the user. The system can retreve more number of mages present n the query data base. The proposed classfer s analyzed wth exstng Sequental Mnmal Optmzaton (SMO) and K earest eghbour classfer K). From the expermental analyss, the proposed classfer outperforms all other classfer taken for examnaton. Keywords: CT Bran mage classfcaton, Quadratc programmng, Lnear SVM, SMO, K.. Introducton Medcal magng s n need of automatc processng for effcentdagnoss wth hgher accuracy. Medcal mages are normally attaned by X-rays, Magnetc Resonance (MRI) Imagng and Computer Tomography (CT). CT s used to analyze the dseases n medcal envronment. [, ]. For dagnoss and montorng, the patent s mages plays mportant tools n medcal magng. A systematc analyss for dagnoss montorng the dsease s one of the most mportant tools n medcne snce t provdes a method for dagnoss, montorng dsease of patents havng the advantage of beng a very fast detecton. As new mage acquston devces are constantly beng developed, to ncrease effcency and produce more accurate nformaton, and data storage, a steady growth of the number of medcal mages produced can be easly nferred [6]. The lack of systematc research on features extracted and ther ole to the classfcaton results forces researchers to select features arbtrarly as nput to ther systems. Content Based Image Retreval (CBIR), a query wth an MRI mage may retreve some PET mages too, although the user [4]. In the last couple of years, several useful research prototypes n the medcal doman have been successfully mplemented wth each work caterng to specfc areas [3,4]. The effectveness of a CBIR system depends on the choce Improved Content Based Image Retreval usng SMO and SVM [5, 6]. In ths paper, Quadratc Program optmzaton (QPO) technque s used to classfy the bran mages usng SVM. The normal and abnormal mage feature s extracted usng MATLAB. The system s used for separatng the data collecton contanng both normal and abnormal mages. Ths results n sgnfcant cost and tme savng by avodng the classfcaton task for large number of databases. The performance of the classfer s examned usng lnear SVM, SMO and K method [7,,4]. The motvaton behnd ths paper s to develop a classfcaton process for evaluatng the classfcaton performance of dfferent classfers n terms of statstcal measures. The paper s organzed as follows. Secton dscusses the proposed methodology usng QPO classfcaton usng SVM for MRI mage classfcaton. Implementaton of the proposed approach and the performance of dfferent classfers re-dscussed n Secton 3. The paper s concluded n Secton 4. Copyrght (c) Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

2 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 4, o, July ISS (Onlne): Methodology The mage classfcaton refers to the groupng of mages nto certan categores. Ths problem seems to be dffcult for the machnes to classfy. Therefore, mage classfcaton usng CBIR system s a challengng task for physcan to dagnoss purpose [8]. The proposed method conssts of Feature extracton and classfcaton. Fgure shows the frame work of the proposed system.. Classfcaton usng Support Vector Machne SVM often provdes better classfcaton results that are wdely used for pattern recognton methods, such as the maxmum lkelhood and neural network classfers. SVM are set of related supervsed learnng methods used for classfcaton and regresson. The property of SVM s, mnmzaton of expermental classfcaton error and maxmzaton of geometrc margn. So t s also called Maxmum Margn Classfers. The hyperplane whch separates the data erect two parallel hyperplane on each sde. Each hyperplane separated maxmze the dstance occurred. Quadratc Programmng (QP) s solved by Radal Bass Functon and Mult-Layer Perceptron classfers. Fg.. Frame work of the proposed system. Feature Extracton To analyze the set of features, the process of transformng the nput data nto the set of features s known as Feature extracton. The extracton of the feature s the most mportant step for representng all the relevant nformaton from the nput mages to accomplsh good qualty result and to proceed further process. In our paper the process of feature extracton s followed accordngly:. To dentfy the boundary regons by contour method,. To extract dagnostc data from the boundary regons. To fnd the rregularty of the mage, the classfcaton s a crtcal task to fnd the accurate results. So to mprove the accuracy, the related features are extracted for better classfcaton. The ntensty hstogram features are analyzed usng mean, varance, skewness, kurtoss, entropy and energy. The categorzaton of mages nto normal and abnormal s done usng statstcal features of mages such as co-occurrence based textural features of mages such as energy, entropy, dfference moment, nverse dfference moment and correlaton. The extracted features are then gven to SVM for quadratc program problem optmzaton. Fg.. Structure of the optmzed SVM The learnng problem settng for SVM s as follows: there s some unknown and nonlnear dependency (mappng, functon) y = f(x) between some hgh-dmensonal nput vector x and scalar output y (or the vector output y as n the case of multclass SVM). The classfcaton problem can be consdered as a two-class problem wthout loss of generalty. The goal s to separate the normal and abnormal mages by a functon whch s nduced from avalable bran database. The lnear classfer s shown n fgure. Ths lnear classfer s termed as the optmal separatng hyperplane... Motvaton Classfyng data s a common task n machne learnng. A group of data present n the database s classfed nto two classes accordngly to fnd the abnormal bran for dagnoss. In the case of support vector machnes, a data pont s vewed as a pdmensonal vector (a lst of p numbers), and to separate such ponts wth a (p )- dmensonal hyperplane. Ths s called a lnear classfer. To classfy the data, many hyperplanes are used. If hyberplane represents the largest separaton between two Copyrght (c) Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

3 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 4, o, July ISS (Onlne): classes then t s called as best hyperplane. Choosng of data from nearest data to an each sde should be maxmzed. If such a hyperplane exsts, t s known as the maxmum-margn hyperplane and the lnear classfer known as maxmum margn classfer; or equvalently, the perceptron of optmal stablty. The followng fgure 3 shows the three hyperplane whch separates the data n to two classes. The H3 does not separate the two classes, H does, wth a small margn and H wth the maxmum margn. Maxmum-margn hyperplane and margns for an SVM traned wth samples from two classes. Samples on the margn are called the support vectors. wx b= where denotes the dot product and W the normal vector to the hyperplane. The parameter determnes the offset of the hyperplane from the orgn along the normal vector w. one want to choose the w and b to maxmze the margn, or dstance between the parallel hyperplanes that are as far apart as possble whle stll separatng the data. These hyperplanes can be descrbed by the equatons w x b= w x b= and Fg. 3 H3 (doesn't separate the two classes). H (separates wth a small margn) and H (separates wth the maxmum margn)... Lnear SVM Some tranng data D, a set of n ponts of the form s gven to a lnear SVM classfer, {(, ) p, {,} } n = D = x y x R y where the y s ether or, ndcatng the class to whch the pont X belongs. Each X s a p-dmensonal real vector. To fnd the maxmum-margn hyperplane that dvdes the ponts havng y=, from those havng, y = -. X s a hyperplane whch s shown n fgure 4. ote that f the tranng data are lnearly separable, we can select the two hyperplanes of the margn n a way that there are no ponts between them and then try to maxmze ther stance. By usng geometry, we fnd the dstance between these two hyperplanes s, so we want to w mnmze w. As we also have to prevent data ponts from fallng nto the margn, we add the followng constrant: for each ether w x b for the frst and second for w x b Ths can be rewrtten as: C We can put ths together to get the optmzaton problem: Mnmze (n w,b) w subject to (for any =,... n ) ( b) y w x..3 Quadratc Equaton Problem Fg.4. Maxmum Margn classfer The process of mnmzng q makes the optmzaton process dffcult. In Quadratc Optmzaton the q s change to Mnmze (n w,b) x subject to (for any =,... n) ( b) y w x Copyrght (c) Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

4 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 4, o, July ISS (Onlne): mn n = w α[ y( w x b) ] wb,, α = The hyperplanes whch dvde the ponts; then all ( ) y w x b. Hence we could fnd the mnmum, and ths mnmum would be reached for all the members of the famly, not only for the best one whch can be chosen solvng the orgnal problem. evertheless the prevous constraned problem can be expressed as n mnmax = w α[ y( w x b) ] wb,, α α = a saddle pont. In dong so all the ponts whch can be separated as y ( w x b) do not matter snce α we must set the correspondng to zero. Ths problem can now besolved by standard quadratc programmng technques n w = αyx Only a few correspondng = wll be greater than zero. The are exactly the support vectors, whch le on the margn and satsfy y ( w x b) =. From ths one can derve that the support vectors also satsfy wx b= / y= y b= wx y whch allows one to defne the offset. In practce, t s more robust to average over all SV vectors: SV ( ) b= w x y SV =..4 Quadratc Equaton Problem The success of SVM depends on the selecton of kernel and ts parameters(c). Commonly Gaussan kernel, whch has a sngle parameter γ wth combnaton of C s often selected by a grd search wth exponentally growng sequences of C and γ. ormally, each combnaton of parameter s cross valdated for best accuracy. The valdated data s fnally gven to the SVM classfer for testng and tranng process.. Steps nvolved n Proposed Methodology. Hyperplane actng as the decson surface s defned as (, ) α d k x x = Where K(x,x ) = φ T (x)φ(x ) represents the nner product of two vectors nduced n the feature space by the nput vector x and nput pattern x pertanng to the th example. Ths term s referred to as nner-product kernel [3]. Where (, ) d ϕ xx = α ( x) ( x), ( x),..., ( x) = m ϕ ϕ ϕ ϕ φ (x) = for all x w o denotes the bas b. The requrement of the kernel K(x,x ) s to satsfy Mercer s theorem. The kernel functon s selected as a polynomal learnng machne. K(x,x ) = ( + x T x ) 3. The Quadratc Program Optmzaton {α } for = to that mnmze the objectve functon Q(α), denoted by α, s determned. Q( α) = α (, ) αα j jdd jk x x = = = j Subject to the followng constrants For =,, = dd j= α C, 4. The lnear weght vector w correspondng to the optmum values of the Lagrange multplers are determned usng the followng formula:, W ( ) α d k x x = φ(x ) s the mage nduced n the feature space due to x. W ( x ) α d κ = Copyrght (c) Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

5 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 4, o, July ISS (Onlne): W represents the optmum bas b. 3. Implementaton and Results The parameters of the maxmum-margn hyperplane are derved by solvng the optmzaton. There exst several specalzed algorthms for quckly solvng the QP problem that arses from SVMs, mostly relyng on heurstcs for breakng the problem down nto smaller, moremanageable chunks. A common method s SMO algorthm, whch breaks the problem down nto -dmensonal sub-problems that may be solved analytcally, elmnatng the need for a numercal optmzaton algorthm. Instead of solvng a sequence of broken down problems, ths approach drectly solves the problem as a whole. To avod solvng a lnear system nvolvng the large kernel matrx, a low rank approxmaton to the matrx s often used n the kernel trck. The nput to the feature extracton algorthm s the bacteral mages. The pattern vectors (features) extracted from the mages s gven as nput to the SVM classfer. Large database are requred for the classfer to perform the classfcaton correctly. In ths system a sample of 5 CT bran mages are collected. 9 mages are taken as normal bran and remanng 6 mages are taken as abnormal bran. mages are used for tranng phase and remanng 5 mages are used for testng. The Classfcaton accuracy and error rate s obtaned by usng the followng formula: Accuracy = (TP+T)/ (TP+T+FP+F)* % are used to measure the performance of the classfer Actual Data Table : The confuson matrx table Predcated Data ormal ormal T FP Abnormal F TP Abnormal Table Classfer Performance Method Senstvty Specfcty Accuracy Tme K S SMO S SVM S QPO The above table shows the dfferent classfer performance. The proposed SVM-QPO gves the better results when compare to other classfer. The followng fgure 5 shows the classfer performance All classfcaton result could have an error rate and on occason wll ether fal to dentfy an abnormalty, or dentfy an abnormalty whch s not present. It s common to descrbe ths error rate by the terms true and false postve and true and false negatve as follows: True Postve (TP): the classfcaton result s postve n the presence of the clncal abnormalty. True egatve (T): the classfcaton result s negatve n the absence of the clncal abnormalty. False Postve (FP): the classfcaton result s postve n the absence of the clncal abnormalty. False egatve (F): the classfcaton result s negatve n the presence of the clncal abnormalty. Table s the confuson matrx table whch defnes varous terms used to descrbe the clncal effcency of a classfcaton based on the terms above and Senstvty = TP/ (TP+F) *% Specfcty = T/ (T+FP) *% Fg.5 Classfer Performance Fg.6 Predcton tme Copyrght (c) Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

6 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 4, o, July ISS (Onlne): The above fgure 6 shows the predcton tme results for dfferent classfer. The followng table3 shows the number of nput mages for tranng phase and testng phase and the correctly classfed mages by Support Vector Machne. The proposed method gves the effcent results when compare to other classfer. Type Table 3: SVM-QPO Classfer Performance o. Of. Input Correctly Effcency Image Classfed mage Testng Tranng Testng Tranng Testng Tranng Total Conclusons The proposed approach usng SVM as a classfer for classfcaton of bran mages provdes a good classfcaton effcency of 96.6% durng tranng phase and 95% effcency durng testng phase. The senstvty, specfcty and accuracy s also mproved. The proposed approach s computatonally effectve and yeld good result. The future work s to mprove the classfcaton accuracy by extractng more features and ncreasng the tranng data set. References []Epaphrodte Uwmana, Automatc classfcaton of medcalmages for Content Based Image Retreval Systems CBIR),AMIA Annual Symposum proceedngs AMIA Symposum AMIA Symposum, Vol.73, o., pp 59, 8. []Chen-Cheng Lee, Sz-Han Chen, and Yu-Chun Chang,Classfcaton of Lver Dsease from CT Images Usng a Support Vector Machne, Journal of Advanced Computatonal Intellgence and Intellgent Informatcs, Vol.,o.4, pp , 7 [3] H. Selvaraj, S. Thamara Selv, D. Selvath3, L. Gewal, Bran MRI Slces Classfcaton Usng Least Squares Support Vector Machne, Vol., o., pp. -33,7. [4]Lahouaou Lalaou, Tayeb Mohammad, Mohamed Chemachema and Abdessalem Hocn, Support Vector Machne (SVM) and the eural etworks for Segmentaton the Magnetc Resonance Imagng, proceedng of IEEE 5 th Internatonal Conference: Scences of Electronc, Technologes of Informaton and Telecommuncatons, March -6,9. [5]Igor F. Amaral, Flpe Coelho, Joaqum F. Pnto da Costa and Jame S. Cardoso, Herarchcal Medcal Image Annotaton Usng SVM-based Approaches, proceedng of IEEE Internatonal conference on Informaton Technology and Applcaton n bomedcne, pp. -5,. [6]Ramesh Babu Dura C., Balaj V., Durasamy V., Improved Content Based Image Retreval usng SMO and SVM Classfcaton Technque, European Journal of Scentfc Research, Vol.69, o.4, pp ,. [7]R.Muraldharan, Dr.C.Chandrasekar, Object Recognton usng SVM-K based on Geometrc Moment Invarant, Internatonal Journal of Computer Trends and Technology- July to Aug Issue, pp.5-,. [8]A.Ramaswamy Reddy, Dr. E.V.Prasad, Dr.L.S.S.Reddy,Abnormalty Detecton of Bran MRI Images usng a ew Spatal FCM Algorthm, Internatonal Journal of Engneerng Scence & Advanced Technology, Vol., o., pp. - 7,. [9]Le Zhang, Fuzong Ln, Bo Zhang, Support Vector Machne Learnng for Image Retreval, Proceedng on IEEE Internatonal conference on Image processng, pp. 7-74,. [] Issam El-aqa et.al, A Support Vector Machne Approach for Detecton of Mcrocalcfcatons, IEEE Transactons On Medcal Imagng, Vol., o.,pp , Dec.. [] Sanghamtra Mohanty, Hmadr andn Das Bebartta, Performance Comparson of SVM and K- for Orya Character Recognton, Internatonal Journal of Advanced Computer Scence and Applcatons, Specal Issue on Image Processng and Analyss, pp. -6,. [] A.Padma, Automatc Classfcaton and Segmentaton of Bran Tumor n CT Images usng Optmal Domnant Gray level Run length Texture Features, Internatonal Journal of advanced Computer Scence and Applcatons, Vol., o., [3] Fe Peng Kehong Yuan Shu Feng Wufan Chen, Proceedng of Internatonal conference on Bonformatcs, pp ,8. [4] Al Reza Fallah, Mohammad Pooyan, Hassan Khotanlou, A new approach for classfcaton of human bran CT mages based on morphologcal operatons, J. Bomedca l Scence and Engneerng,Vol.3, pp. 78-8,., Copyrght (c) Internatonal Journal of Computer Scence Issues. All Rghts Reserved.