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• How do I classify using GLCM and SVM Classifier in Matlab?

  - by Gomathi

  I'm on a project of liver tumor segmentation and classification. I used Region Growing and FCM for liver and tumor segmentation respectively. Then, I used Gray Level Co-occurence matrix for texture feature extraction. I have to use Support Vector Machine for Classification. But I don't know how to normalize the feature vectors. Can anyone tell how to program it in Matlab? To the GLCM program, I gave the tumor segmented image as input. Was I correct? If so, I think, then, my output will also be correct. My glcm coding, as far as I have tried is, I = imread('fzliver3.jpg'); GLCM = graycomatrix(I,'Offset',[2 0;0 2]); stats = graycoprops(GLCM,'all') t1= struct2array(stats) I2 = imread('fzliver4.jpg'); GLCM2 = graycomatrix(I2,'Offset',[2 0;0 2]); stats2 = graycoprops(GLCM2,'all') t2= struct2array(stats2) I3 = imread('fzliver5.jpg'); GLCM3 = graycomatrix(I3,'Offset',[2 0;0 2]); stats3 = graycoprops(GLCM3,'all') t3= struct2array(stats3) t=[t1;t2;t3] xmin = min(t); xmax = max(t); scale = xmax-xmin; tf=(x-xmin)/scale Was this a correct implementation? Also, I get an error at the last line. My output is: stats = Contrast: [0.0510 0.0503] Correlation: [0.9513 0.9519] Energy: [0.8988 0.8988] Homogeneity: [0.9930 0.9935] t1 = Columns 1 through 6 0.0510 0.0503 0.9513 0.9519 0.8988 0.8988 Columns 7 through 8 0.9930 0.9935 stats2 = Contrast: [0.0345 0.0339] Correlation: [0.8223 0.8255] Energy: [0.9616 0.9617] Homogeneity: [0.9957 0.9957] t2 = Columns 1 through 6 0.0345 0.0339 0.8223 0.8255 0.9616 0.9617 Columns 7 through 8 0.9957 0.9957 stats3 = Contrast: [0.0230 0.0246] Correlation: [0.7450 0.7270] Energy: [0.9815 0.9813] Homogeneity: [0.9971 0.9970] t3 = Columns 1 through 6 0.0230 0.0246 0.7450 0.7270 0.9815 0.9813 Columns 7 through 8 0.9971 0.9970 t = Columns 1 through 6 0.0510 0.0503 0.9513 0.9519 0.8988 0.8988 0.0345 0.0339 0.8223 0.8255 0.9616 0.9617 0.0230 0.0246 0.7450 0.7270 0.9815 0.9813 Columns 7 through 8 0.9930 0.9935 0.9957 0.9957 0.9971 0.9970 ??? Error using ==> minus Matrix dimensions must agree. The images are:

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• How to properly install libsvm-3.11?

  - by Gomathi

  I'm using libvm-3.11. I downloaded it at http://www.csie.ntu.edu.tw/~cjlin/libsvm/ and extracted. Then I set path in Matlb. After that whenever I run my program, it gives the same error always. ??? Invalid MEX-file 'E:\Gomu\Gomu General\final yr Project\proj\libsvm-3.11\windows\svmtrain.mexw32': The specified module could not be found. . Error in == trysvm at 6 svmStruct = svmtrain(T,TrainMat,'showplot',true); What should I do?

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• How do I classify using SVM Classifier in Matlab?

  - by Gomathi

  I'm on a project of liver tumor segmentation and classification. I used Region Growing and FCM for liver and tumor segmentation respectively. Then, I used Gray Level Co-occurence matrix for texture feature extraction. I have to use Support Vector Machine for Classification. But I don't know how to normalize the feature vectors. Can anyone tell how to program it in Matlab? To the GLCM program, I gave the tumor segmented image as input. Was I correct? If so, I think, then, my output will also be correct. I gave the parameters exactly as in the example provided in the documentation itself. The output I obtained was stats = autoc: [1.857855266614132e+000 1.857955341199538e+000] contr: [5.103143332457753e-002 5.030548650257343e-002] corrm: [9.512661919561399e-001 9.519459060378332e-001] corrp: [9.512661919561385e-001 9.519459060378338e-001] cprom: [7.885631654779597e+001 7.905268525471267e+001] cshad: [1.219440700252286e+001 1.220659371449108e+001] dissi: [2.037387269065756e-002 1.935418927908687e-002] energ: [8.987753042491253e-001 8.988459843719526e-001] entro: [2.759187341212805e-001 2.743152140681436e-001] homom: [9.930016927881388e-001 9.935307908219834e-001] homop: [9.925660617240367e-001 9.930960070222014e-001] maxpr: [9.474275457490587e-001 9.474466930429607e-001] sosvh: [1.847174384255155e+000 1.846913030238459e+000] savgh: [2.332207337361002e+000 2.332108469591401e+000] svarh: [6.311174784234007e+000 6.314794324825067e+000] senth: [2.663144677055123e-001 2.653725436772341e-001] dvarh: [5.103143332457753e-002 5.030548650257344e-002] denth: [7.573115918713391e-002 7.073380266499811e-002] inf1h: [-8.199645492654247e-001 -8.265514568489666e-001] inf2h: [5.643539051044213e-001 5.661543271625117e-001] indnc: [9.980238521073823e-001 9.981394883569174e-001] idmnc: [9.993275086521848e-001 9.993404634013308e-001] The thing is, I run the program for three images. But all three gave me the same output. When I used graycoprops() stat = Contrast: 4.721877658740964e+005 Correlation: -3.282870417955449e-003 Energy: 8.647689474127760e-006 Homogeneity: 8.194621855726478e-003 stat = Contrast: 2.817160447307697e+004 Correlation: 2.113032196952781e-005 Energy: 4.124904827799189e-004 Homogeneity: 2.513567163994905e-002 stat = Contrast: 7.086638436309059e+004 Correlation: 2.459637878221028e-002 Energy: 4.640677159445994e-004 Homogeneity: 1.158305728309460e-002 The images are:

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• How do I classify using SVM Classifier?

  - by Gomathi

  I'm doing a project in liver tumor classification. Actually I initially used Region Growing method for liver segmentation and from that I segmented tumor using FCM. I,then, obtained the texture features using Gray Level Co-occurence Matrix. My output for that was stats = autoc: [1.857855266614132e+000 1.857955341199538e+000] contr: [5.103143332457753e-002 5.030548650257343e-002] corrm: [9.512661919561399e-001 9.519459060378332e-001] corrp: [9.512661919561385e-001 9.519459060378338e-001] cprom: [7.885631654779597e+001 7.905268525471267e+001] Now how should I give this as an input to the SVM program. function [itr] = multisvm( T,C,tst ) %MULTISVM(2.0) classifies the class of given training vector according to the % given group and gives us result that which class it belongs. % We have also to input the testing matrix %Inputs: T=Training Matrix, C=Group, tst=Testing matrix %Outputs: itr=Resultant class(Group,USE ROW VECTOR MATRIX) to which tst set belongs %----------------------------------------------------------------------% % IMPORTANT: DON'T USE THIS PROGRAM FOR CLASS LESS THAN 3, % % OTHERWISE USE svmtrain,svmclassify DIRECTLY or % % add an else condition also for that case in this program. % % Modify required data to use Kernel Functions and Plot also% %----------------------------------------------------------------------% % Date:11-08-2011(DD-MM-YYYY) % % This function for multiclass Support Vector Machine is written by % ANAND MISHRA (Machine Vision Lab. CEERI, Pilani, India) % and this is free to use. email: [email protected] % Updated version 2.0 Date:14-10-2011(DD-MM-YYYY) u=unique(C); N=length(u); c4=[]; c3=[]; j=1; k=1; if(N>2) itr=1; classes=0; cond=max(C)-min(C); while((classes~=1)&&(itr<=length(u))&& size(C,2)>1 && cond>0) %This while loop is the multiclass SVM Trick c1=(C==u(itr)); newClass=c1; svmStruct = svmtrain(T,newClass); classes = svmclassify(svmStruct,tst); % This is the loop for Reduction of Training Set for i=1:size(newClass,2) if newClass(1,i)==0; c3(k,:)=T(i,:); k=k+1; end end T=c3; c3=[]; k=1; % This is the loop for reduction of group for i=1:size(newClass,2) if newClass(1,i)==0; c4(1,j)=C(1,i); j=j+1; end end C=c4; c4=[]; j=1; cond=max(C)-min(C); % Condition for avoiding group %to contain similar type of values %and the reduce them to process % This condition can select the particular value of iteration % base on classes if classes~=1 itr=itr+1; end end end end Kindly guide me. Images:

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• Ant build scripts

  - by gomathi

  I am using ant script for generating war file, it will generate the war file. please see the below script If it generates a new war file, then i want to have a property to set the value as "newupdates" otherwise i want to know "noupdates"

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