Question

我正在进行肝肿瘤分类项目。实际上我最初使用区域生长方法进行肝脏分割，并使用FCM分割肿瘤。然后，我使用Gray Level Co-occurence Matrix获得纹理特征。我的输出是

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]

现在我应该如何将其作为SVM程序的输入。

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: anand.mishra2k88@gmail.com

% 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

请指导我。

图片： tumor1 tumor2 tumor3

Answer 1

您必须获取所有您获得的特征值，并将它们连接成一个特征向量。然后对于SVM，应该对特征进行规范化，以便每个维度中的值在-1和1之间变化，如果我没记错的话。我认为libsvm具有进行规范化的功能。

因此，假设您的特征向量最终具有N个维度，并且您有M个训练实例，则您的训练集应为M×N矩阵。然后，如果您有P个测试实例，那么您的测试集应该是P x N矩阵。

Answer 2

我还建议你一个非常流行的SVM实现，称为SVMLight http://svmlight.joachims.org/。

您可以在网站上找到有关如何使用它的示例。也可以使用Mex-matlav包装纸。

正如Dima所指出的，你需要连接这些功能吗？

你能告诉我你用于肝肿瘤分类的数据集吗？它是否可以公开下载？

如何使用SVM分类器进行分类？

2 个答案: