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:
