ARTICLE IN PRESS. computer methods and programs in biomedicine xxx (2007) xxx xxx. journal homepage:

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1 computer methods and programs n bomedcne xxx (2007) xxx xxx journal homepage: Improvng bran tumor characterzaton on MRI by probablstc neural networks and non-lnear transformaton of textural features Pantels Georgads a,, Donss Cavouras b, Ioanns Kalatzs b, Antons Daskalaks b, George C. Kagads b, Korala Sfak c, Menelaos Malamas c, George Nkfords a, Ekatern Solomou d a Medcal Image Processng and Analyss (MIPA) Group, Laboratory of Medcal Physcs, School of Medcne, Unversty of Patras, Ro GR-26503, Greece b Medcal Image and Sgnal Processng Laboratory, Department of Medcal Instrumentaton Technology, Technologcal Educaton Insttuton of Athens, Ag. Spyrdonos Street, Agaleo GR-12210, Athens, Greece c 251 General Hellenc Arforce Hosptal, MRI Unt, Katehak, Athens, Greece d Department of Radology, School of Medcne, Unversty of Patras, Ro GR-26503, Greece artcle nfo abstract Artcle hstory: Receved 24 January 2007 Receved n revsed form 12 October 2007 Accepted 13 October 2007 Keywords: Bran tumors MRI Textural features Pattern classfcaton The am of the present study was to desgn, mplement and evaluate a software system for dscrmnatng between metastatc and prmary bran tumors (glomas and menngomas) on MRI, employng textural features from routnely taken T1 post-contrast mages. The proposed classfer s a modfed probablstc neural network (PNN), ncorporatng a non-lnear least squares features transformaton (LSFT) nto the PNN classfer. Thrty-sx textural features were extracted from each one of 67 T1-weghted post-contrast MR mages (21 metastases, 19 menngomas and 27 glomas). LSFT enhanced the performance of the PNN, achevng classfcaton accuraces of 95.24% for dscrmnatng between metastatc and prmary tumors and 93.48% for dstngushng glomas from menngomas. To mprove the generalzaton of the proposed classfcaton system, the external cross-valdaton method was also used, resultng n 71.43% and 81.25% accuraces n dstngushng metastatc from prmary tumors and glomas from menngomas, respectvely. LSFT mproved PNN performance, ncreased class separablty and resulted n dmensonalty reducton Elsever Ireland Ltd. All rghts reserved. 1. Introducton Accordng to a recent statstcal report publshed by the Central Bran Tumor Regstry of the Unted States (CBTRUS), approxmately 39,550 people were newly dagnosed wth prmary bengn and prmary malgnant bran tumors [1,2] n 2002 [3]. Furthermore, n 2000, more than 81,000 people, n the Unted States alone, were lvng wth a prmary malgnant bran tumor and 267,000 were lvng wth a prmary bengn bran tumor. The same report ndcates that the ncdence rate of prmary bran tumors, whether bengn or malgnant, s 14 per 100,000, whle medan age at dagnoss s 57 years [3]. Secondary or metastatc bran tumors [1], n contrast to prmary bran tumors, orgnate n tssues outsde the central Correspondng author. Tel.: E-mal addresses: pgeorgads@med.upatras.gr (P. Georgads), cavouras@teath.gr (D. Cavouras). URL: (P. Georgads) /$ see front matter 2007 Elsever Ireland Ltd. All rghts reserved. do: /j.cmpb

2 2 computer methods and programs n bomedcne xxx (2007) xxx xxx nervous system and are a common complcaton of systemc cancer. Bran metastases outnumber prmary bran tumors and are currently classfed as the most frequent ntracranal tumors. Other studes ndcate that bran metastases occur n 20 40% of all cancer patents and that more than 100,000 ndvduals per year wll develop bran metastases [3]. Today, magng technques, lke magnetc resonance magng (MRI), are used to locate the poston and extent of bran tumors. MRI can provde nformaton about bran tssues, from a varety of exctaton sequences. Compared wth other dagnostc magng modaltes, such as computerzed tomography, MRI provdes superor contrast for dfferent bran tssues [4]. Addtonally, MR mages encapsulate valuable nformaton regardng numerous tssue parameters (proton densty, spn lattce (T1) and spn spn (T2) relaxaton tmes, flow velocty and chemcal shft), whch lead to more accurate bran tssue characterzaton. These unque advantages have characterzed MRI as the method of choce n bran tumor studes [5]. Bran tumor characterzaton s a process that requres a complcated assessment of the varous MR mage features and s typcally performed by experenced radologsts. An expert radologst performs ths task wth a sgnfcant degree of precson and accuracy, despte the nherently subjectve nature of many of the decsons assocated wth ths process. Nevertheless, n the effort to delver more effectve treatment, clncans are contnuously seekng for greater accuracy n the pathologcal characterzaton of bran tssues from magng nvestgatons [6]. 2. Background and desgn consderatons To ths need, mage analyss technques have been employed n prevous studes for the extracton of dagnostc nformaton from MR mages [6 8]. These studes have employed pattern recognton and texture analyss technques to characterze human bran tumors. In a recent study [9], an SVM-based classfcaton system has acheved 95% overall accuracy n dscrmnatng between glomas and menngomas. In another study [7], the herarchcal ascendng classfcaton wth correspondence factoral analyss has been used for dscrmnatng between dfferent tumor types, wth accuraces rangng between 49% (tumors versus oedemas) and 63% (bengn versus malgnant tumors). In a prevous study [6], dscrmnant analyss and the k-nearest neghbor classfer have been adopted for dstngushng between human bran tumors and oedematous tssues, achevng maxmum overall accuracy of 95%. In another study [10], several non-pctoral dagnostc factors, such as age, oedema, blood supply, calcfcaton and haemorrhage, that were employed n an SVM classfcaton scheme, have been found to be mportant n assessng bran glomas. Fnally, more recent studes have employed MR spectroscopc features [11 16] or combnaton of textural and spectroscopc features to dscrmnate between varous types of bran tumors achevng accuraces up to 99% [15]. These studes, utlzng ether statstcal analyss technques or state of art classfers, have shown that MR spectroscopc features can provde an added value n the accurate characterzaton of bran tumors. However, obtanng of MR spectroscopy data s not a readly avalable functon n all MRI unts and addtonally t requres expertse for sgnal condtonng. The am of the present study was to desgn, mplement and evaluate a pattern recognton system, whch, by analyzng routnely taken T1 post-contrast MR mages, would mprove bran tumor classfcaton accuracy. Employng a two-level herarchcal decson tree, dstncton between metastatc and prmary bran tumors and between glomas and menngomas were performed at the frst and second level of the decson tree, respectvely. Addtonally, the present study demonstrated that by employng textural features from MR mages and by condtonng those features by means of a non-lnear least squares features transformaton (LSFT), the performance of the probablstc neural network (PNN) classfer was boosted sgnfcantly. 3. System descrpton 3.1. Data acquston A total number of 67 T1-weghted gadolnum-enhanced MR mages were obtaned from the Hellenc Arforce Hosptal wth verfed ntracranal tumors, usng a SIEMENS-Sonata 1.5 Tesla MR Unt. The mage dataset comprsed 21 metastases, 19 menngomas and 27 glomas. From each case, only T1-weghted post-contrast (Gadolnum) mages, wth spn echo (SE) sequence, echo tme (TE = 15 ms) and repetton tme (TR = 500 ms), were used for further analyss. The reason for employng T1 post-contrast mages s the ncreased dagnostc nformaton that they encapsulate n comparson to pre-contrast T1 or T2 weghted mages. More specfcally, contrast admnstraton asssts n the separaton of tumor from oedema mprovng vsualzaton, localzaton and tumor margn delneaton. Contrast enhancement s ntense because of the h-degree of blood bran barrer (BBB) dsrupton [17]. Transverse mages were selected through the tumor s center by an expert radologst (M.M.) Feature extracton and reducton Utlzng these mages, the radologst specfed square regons of nterest (ROIs) wthn the tumor area. Each ROI was semautomatcally drawn around a pxel by a smple clck on the mouse. From each ROI, a seres of 36 features were extracted; 4 features from the ROI s hstogram, 22 from the co-occurrence matrces [18] and 10 from the run-length matrces [19]. All features were normalzed to zero mean and unt standard devaton [20], accordng to relaton (1) x = x m std, (1) where x and x are the th feature values before and after the normalzaton, respectvely, and m and std are the mean value and standard devaton, respectvely, of feature x over all patterns and all classes. Regardng the latter, only 2-class classfcaton problems (prmary versus secondary and glomas versus menngomas) were consdered, embedded n a twolevel herarchcal decson-tree structure. In order to reduce feature dmensonalty, the non-parametrc Wlcoxon rank-

3 computer methods and programs n bomedcne xxx (2007) xxx xxx 3 Fg. 1 Custom made applcaton for mage and ROI acquston. sum test was employed [21]. Accordngly, only features of hgh dscrmnatory ablty (p < 0.001), between the patterns of two classes, were selected to feed the classfcaton scheme. Images were obtaned from the hosptal s database n DICOM V3.0 format, and usng custom software developed n C++, mages were read, dsplayed and ROIs were extracted for further processng (Fg. 1) Least squares features transformaton-probablstc neural network (LSFT-PNN) classfer The probablstc neural network s a non-parametrc feedforward neural network classfer, wth dscrmnant functon [22] gven by: N 1 1 [ ] g (x) = exp (x x j) T (x x j ) (2) d/2 d N 2 2, (2) where x s the pattern to be classfed, x j are the tranng patterns, s the spread of the Gaussan actvaton functon, N s the number of tranng patterns n class and d s the dmensonalty of pattern vectors. Tranng patterns x j, pror to enterng the PNN classfer, were transformed by means of a non-lneal least squares feature transformaton technque, to render classes more separable by clusterng the patterns of each class around arbtrary pre-selected ponts. The proposed LSFT method s an extenson of the lnear least squares mappng technque, ntroduced by [23]. Intally, pattern vectors were extended wth n-degree elements. Let x =[x 1 x 2...x d ] be a pattern vector, where d s the feature space dmensonalty and x, =1, 2,..., d are the feature values of pattern. The pattern vector x was augmented wth the up to nth degree elements so that, fnally, the augmented pattern vector ˆx conssted of all polynomal k-degree terms (k =1,2,..., n) of the form: (3)

4 4 computer methods and programs n bomedcne xxx (2007) xxx xxx The dmensonalty of the extended pattern vector ( ˆx) s equal to [20]: (d + n)! ˆd = 1. (4) d!n! For the formulaton of a LSFT 2-class problem, let space S, wth dmensonalty equal to the number of classes (K = 2), and let P =[p 1 p 2 ], = 1, 2 be arbtrary defned ponts n space S, correspondng to each class. A transformaton T s sought such that the total mean square error between the transformed extended vectors (T ˆx j ) and P s mnmzed as follows (assumng equal a pror probabltes for each class ): T (T ˆx N j P ) (T ˆx j P ) = 0, (5) =1 or [ T ( ˆx N j T T ˆx j ) 2 T ( ˆx j T P ) + T (P P )] = 0 =1 where K s the number of classes, N s the number of patterns of class and ˆx j are the n-degree extended tranng patterns of class. Applyng basc matrx algebra to the terms of (6): T ( ˆx j T T ˆx j ) = 2T( ˆx j ˆx j ) (7) T ( ˆx j T P ) = P ˆx j (8) T (P P ) = 0 (9) Eqs. (6) (9) gve: [2T ( ˆx N j x j ) 2P x j ] = 0 (10) =1 or T ˆx N j x j =1 whch results n: (6) P N x j = 0 (11) =1 1 T = P N ˆx j ˆx N j ˆx j. (12) =1 =1 Transformaton matrx T s a K ˆd matrx, so the decson space dmensonalty s equal to the number of bran tumor classes. Followng the LSFT procedure, patterns ˆx j were fed nto the PNN classfer, resultng n the fnal dscrmnant functon of Fg. 2 Herarchcal tree classfcaton scheme. the LSFT-PNN classfer: N 1 1 g (x) = exp (2)ˆd/2 ˆd N 3.4. Desgn of the classfcaton scheme [ ] (T ˆx T ˆx j) (T ˆx T ˆx j ) 2 2. (13) A two level herarchcal decson tree was desgned to dscrmnate the metastatc bran tumor cases from the glomas and menngomas (prmary bran tumors) cases (Fg. 2). At the frst level, the glomas and the menngomas cases were grouped nto the prmary bran tumor class and were classfed aganst the metastatc bran tumor cases. At the second level, the prmary tumor cases were further classfed nto cases wth glomas and menngomas. At each level, classfcaton was performed usng two dfferent LSFT-PNN classfers. At the frst level of the decson tree, a thrd degree (cubc) LSFT-PNN (k = 3 n Eq. (3)) was employed to dscrmnate between prmary and metastatc tumors whle, at the second level, a second degree (quadratc) LSFT-PNN (k =2 n Eq. (3)) was used to classfy glomas and menngomas. The choce of the classfer s degree was made on the bass of optmal classfcaton, followng a multple expermentaton procedure. Pror to enterng the classfcaton system, each classfer was optmzed employng the avalable dataset. Optmzaton was performed, separately at both levels of the decson tree, by exhaustvely combnng (n all possble combnatons of 2, 3, etc., features) the statstcally reduced feature vectors (10 features of hgh dscrmnatory power (p < 0.001) were retaned) and by usng the leave-one-out method (LOO) [16], for assessng the performance of each feature combnaton. To avod overfttng condtons, whch may occur by usng the same dataset n the feature selecton and system evaluaton stages, the external cross-valdaton (ECV) method [24] was also used. Accordngly, the dataset was splt n two sets, one was used for optmum classfer desgn (2/3 of the dataset)

5 computer methods and programs n bomedcne xxx (2007) xxx xxx 5 Table 2a Classfcaton results for dscrmnatng prmary and secondary bran tumors employng the LOO method Prmary bran tumors Secondary bran tumors Overall accuracy PNN Lnear LSFT-PNN SVM-RBF ANN Cubc LSFT-PNN Fg. 3 Scatter dagram of the classes nvolved. and the other for evaluaton (1/3 of the dataset). Optmum classfer desgn was acheved by employng: () the Wlcoxon non-parametrc test for feature reducton and () the LOO and exhaustve search methods for determnng the hghest classfcaton accuracy wth the least number of features. That optmum classfer desgn was next used to classfy the evaluaton subset. Ths cycle (desgn-classfcaton) was repeated ten tmes, each tme pckng the tranng subset randomly and formng the evaluaton subset by the remanng data. Fnally, classfcaton accuracy results were averaged for assessng the generalzaton performance of the proposed method. Ths type of classfer tranng requred several hours of processng tme, whle classfcaton tme, once the system has been traned, was nfntesmal. The overall accuracy of the classfcaton system, n dscrmnatng metastatc bran tumors from glomas and menngomas cases, was determned by multplyng the system s performance at each level [20]. 4. Expermental results Fg. 3 shows a scatter dagram of the classes nvolved. The complexty of the problem has led us to adopt a herarchcal decson tree structure (see Fg. 2). The overall classfcaton accuracy at the frst level of the decson tree was 94.03% employng the cubc LSFT-PNN classfer. Indvdual accuraces n dscrmnatng between prmary and secondary bran tumors were 93.48% and 95.24%, respectvely (Table 1). Best feature vector, used for the optmal desgn of the cubc LSFT-PNN classfer, comprsed the mean value, entropy, and Table 2b Classfcaton results for dscrmnatng prmary and secondary bran tumors utlzng the ECV method (averaged results after ten repettons) Prmary bran tumors Secondary bran tumors Overall accuracy PNN Lnear LSFT-PNN SVM-RBF ANN Cubc LSFT-PNN dfference entropy. Employng the ECV method, the mean overall accuracy of the cubc LSFT-PNN classfer was 78.26%, whle the mean accuraces for prmary and secondary bran tumors dscrmnaton were 81.25% and 71.43%, respectvely. The performance of the cubc LSFT-PNN algorthm, used at the frst level of the decson tree, was tested aganst the PNN, the lnear LSFT-PNN, the Support Vector Machnes wth Radal Bass Functon kernel (SVM-RBF) and the Artfcal Neural Network (ANN) classfers, whch were traned n a smlar manner to the cubc LSFT-PNN classfer. Comparatve classfcaton results employng the LOO and the ECV methods are presented n Tables 2a and 2b, respectvely, as well as n Fg. 4. Fgs. 5 8 show scatter dagrams dsplayng prmary and secondary tumor class separaton for the cubc LSFT-PNN, the PNN, the SVM-RBF and the ANN classfers. At the second level of the decson tree, employng the quadratc LSFT-PNN classfer, dscrmnaton accuracy Table 1 Cubc LSFT-PNN classfer truth table for dscrmnatng prmary and secondary tumors Prmary bran tumors Secondary bran tumors Accuracy Prmary bran tumors Secondary bran tumors Overall accuracy Fg. 4 Overall classfcaton accuracy of the algorthms used for dscrmnatng prmary and secondary tumors.

6 6 computer methods and programs n bomedcne xxx (2007) xxx xxx Fg. 5 Scatter dagram of the optmum feature combnaton of the cubc LSFT-PNN classfer and the correspondng decson boundary for dscrmnatng prmary and secondary tumors. Fg. 6 Scatter dagram of the optmum feature combnaton of the PNN classfer and the correspondng decson boundary for dscrmnatng prmary and secondary tumors. Fg. 8 Scatter dagram of the optmum feature combnaton of the ANN classfer and the correspondng decson boundary for dscrmnatng prmary and secondary tumors. between the two types of prmary bran tumors (glomas and menngomas) was 100% (Table 3). The best feature vector, employed for the optmal desgn of the quadratc LSFT-PNN classfer, comprsed the mean value, angular second moment and the nverse dfference moment. Utlzng the ECV technque, the mean overall accuracy of the quadratc LSFT-PNN classfer was 99.33%, whle the mean accuraces for glomas and menngomas dscrmnaton were 88.89% and 100%, respectvely. The classfcaton accuracy of the quadratc LSFT-PNN classfcaton scheme, used at the second level of the decson tree, was tested aganst that of the PNN, the lnear LSFT-PNN, the SVM-RBF and the ANN classfers. Both PNN and lnear LSFT- PNN msclassfed one gloma case resultng n 97.83% overall accuracy whle SVM-RBF and ANN classfers acheved overall dscrmnaton accuracy of 100% and 93.43%, respectvely (Tables 4a and 4b) (Fg. 9). Table 3 Quadratc LSFT-PNN truth table for dscrmnatng glomas and menngomas Glomas Menngomas Accuracy Glomas Menngomas Overall accuracy 100 Table 4a Classfcaton results for dscrmnatng glomas and menngomas employng the LOO method Glomas Menngomas Overall accuracy Fg. 7 Scatter dagram of the optmum feature combnaton of the SVM-RBF classfer and the correspondng decson boundary for dscrmnatng prmary and secondary tumors. PNN Lnear LSFT-PNN SVM-RBF ANN Quadratc LSFT- PNN

7 computer methods and programs n bomedcne xxx (2007) xxx xxx 7 Table 4b Classfcaton results for dscrmnatng glomas and menngomas utlzng the ECV method (averaged results after ten repettons) Glomas Menngomas Overall accuracy PNN Lnear LSFT-PNN SVM-RBF ANN Quadratc LSFT- PNN Fg. 11 Scatter dagram of the optmum feature combnaton of the PNN classfer and the correspondng decson boundary for dscrmnatng glomas and menngomas. Fg. 9 Overall classfcaton accuracy of the algorthms used for dscrmnatng glomas and menngomas. Fg. 12 Scatter dagram of the optmum feature combnaton of the SVM-RBF classfer and the correspondng decson boundary for dscrmnatng glomas and menngomas. gomas can be obtaned by multplyng the correspondng accuraces acheved at each level of the decson tree [20]. Consequently, classfcaton accuraces were 95.24% for the metastatc and 93.48% for both glomas and menngomas bran tumor cases, whle employng the ECV method the classfcaton accuraces were 71.43% for the metastatc, 72.22% for glomas and 81.25% for menngomas. Fg. 10 Scatter dagram of the optmum feature combnaton of the quadratc LSFT-PNN classfer and the correspondng decson boundary for dscrmnatng glomas and menngomas. Fgs show scatter dagrams dsplayng glomas and menngomas class separaton employng the quadratc LSFT- PNN, PNN, SVM-RBF and ANN classfers. The overall accuraces of the classfcaton system n dscrmnatng metastatc tumors from glomas and menn- 5. Dscusson The LSFT-PNN and the PNN classfcaton schemes were optmzed wth respect to parameter settngs and avalable feature data. The spread of Gaussan functon for the LSFT-PNN and the PNN classfers was expermentally set equal to = 0.3. In accordance wth our fndngs, the LSFT-PNN outperformed the PNN at both levels of the decson tree. At the frst level, the LSFT-PNN acheved a senstvty of 93.48% aganst PNN s 86.96% n correctly characterzng prmary tumors, assgnng three more prmary bran tumors to the approprate

8 8 computer methods and programs n bomedcne xxx (2007) xxx xxx Fg. 13 Scatter dagram of the optmum feature combnaton of the ANN classfer and the correspondng decson boundary for dscrmnatng glomas and menngomas. class. Ths s mportant, snce the precson of such a decson may be crucal n patent management. On the other hand, the specfcty acheved by both classfers n assgnng the metastatc bran tumors to the correct classes was the same (95.24%), both mssng out only one secondary bran tumor. Agan, ths s of value snce metastatc tumors requre specfc treatment protocols, such as radaton therapy and chemotherapy, whle prmary tumors may also requre surgcal nterventon [25,26]. The best features combnaton of the cubc LSFT-PNN classfer, at the frst level of the decson tree, expresses the sgnal strength (mean value) and the degree of the n-homogenety (entropy and dfference entropy) n the gray-tones of the ROIs. In a prevous study [13] employng only MR spectroscopc data and the LS-SVM classfcaton algorthm, precsons n dstngushng between metastatc bran tumors and menngomas or globlastomas or astrocytomas were 97%, 59% and 96%, respectvely. Our fndngs are comparable, however employng solely textural features from the T1-contrast enhanced MR mages. At the second level of the decson tree, the quadratc LSFT- PNN dscrmnated correctly all glomas and menngomas cases whle the PNN classfer faled to classfy correctly one gloma case. The best features combnaton of the quadratc LSFT-PNN expresses the sgnal strength (mean value) and a measure of the homogenety (angular second moment and nverse dfference moment) n the gray-tones of the ROIs [18]. These textural characterstcs are related to textural parameters that physcans employ n dagnoss and they are proportonal to the textural mprnt of these two types of bran tumors,.e. glomas have heterogeneous texture whle menngomas appear to be homogeneous n MR magng. In a recent study [9], an SVM-based classfcaton system acheved 95% overall accuracy n dscrmnatng between glomas and menngomas, employng as features mage ntenstes from four sequences (T1, T2, PD, GD). When MR spectra from the lesons were also ncluded as features, classfcaton accuracy reached 99.8%. These results are comparable wth our fndngs regardng dscrmnaton between glomas and menngomas (Table 3), where we have employed solely textural features from T1 post-contrast MR mages. Consderng the results, t can be clamed that the non-lnear LSFT-PNN outperforms the PNN and the lnear LSFT-PNN. Ths may be attrbuted to the ncreased class separablty that the LSFT procedure provdes, especally when non-lnear terms are ntroduced n the classfer s dscrmnant functon. Another advantage of the LSFT-PNN s the dmensonalty reducton, equal to the number of classes, ndependently of the number of features, whch leads to more robust classfcaton. The computatonal requrements of the LSFT-PNN classfer are comparable to those of the PNN, as the addtonal tme requred to perform the LSFT procedure s ganed n the classfcaton step, due to the reduced dmensonalty of the problem. Addtonally, the proposed non-lnear LSFT-PNN algorthm was compared aganst the SVM-RBF and the ANN classfers at both levels of the herarchcal decson tree. Judgng from the results, the proposed algorthm acheved hgher dscrmnaton accuraces than the SVM-RBF, at both levels of the decson tree, whle ts precson was close to the ANN classfer n dscrmnatng prmary from secondary tumors. However, t must be noted that both SVM-RBF and ANN classfers requred a sgnfcant amount of processng tme n ther tranng stage. The computatonal tmes requred for the tranng and evaluaton procedures (10 repettons of ECV employng LOO and exhaustve search) were, approxmately, 40 mn for the proposed LSFT-PNN algorthm, 16 h for the SVM-RBF classfer and 11 h for the ANN classfer (employng the sequental forward selecton technque [20], snce the exhaustve search was unrealstcally tme demandng for the ANN). Ths may be attrbuted to ther nternal optmzaton procedures,.e. the sequental optmzaton procedure for the SVM-RBF and the back-propagaton procedure for the ANN. On the other hand, the proposed algorthm does not requre optmzaton, renderng the classfcaton system fast and effcent n ts tranng. Employng the ECV method, the overall and ndvdual dscrmnaton accuraces were decreased. However, the adopton of ths method rendered the system more general n ts behavor regardng the classfcaton of new datasets. The overall dscrmnaton accuracy decrement usng the ECV method was n accordance wth [24]. The determnaton of a unque best feature vector was not possble employng the ECV technque as, at each repetton, dfferent feature vectors were produced. However, most of those features were related to texture homogenety of the ROIs. Acknowledgement Fundng by the Unversty of Patras Research Commttee under the basc research program K. Karatheodor, project ttle Computer Asssted Dagnoss of Bran Tumors based on Statstcal Methods and Pattern Recognton Technques s gratefully acknowledged. references [1] L.S. Ashby, M.M. Troester, W.R. Shapro, Central nervous system tumors, Update Cancer Therapeut. 1 (4) (2006)

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