i want to the classification using svm ? but only the image is getting segmented and it giving error" Error in saegmentation (line 75) [CA1,CH1,CV1,CD1] = dwt2(I,'db4');".so can you please help me to resolve this error

Question

amit pandey 2018-2-2

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/380310-i-want-to-the-classification-using-svm-but-only-the-image-is-getting-segmented-and-it-giving-error

评论： Rik 2020-12-10

% Project Title: Brain MRI Classification using DWT & PCA close all clc clear all [filename,pathname] = uigetfile({'.jpg'},'C:\Users\rakshya\dowloads\brain_tumor\brain_tumor\brain_tumor_code\maligant\1Perfect.jpgIM-0001-0010.dcm'); I = imread([pathname,filename]); figure, imshow(I); title('Brain MRI Image'); I = imresize(I,[200,200]);

% Convert to grayscale gray = rgb2gray(I);

% Otsu Binarization for segmentation level = graythresh(I); img = im2bw(I,level); figure, imshow(img);title('Otsu Thresholded Image');

% K means Clustering to segment tumor

cform = makecform('srgb2lab'); % Apply the colorform lab_he = applycform(I,cform);

% Classify the colors in a*b* colorspace using K means clustering. % Since the image has 3 colors create 3 clusters. % Measure the distance using Euclidean Distance Metric. ab = double(lab_he(:,:,2:3)); nrows = size(ab,1); ncols = size(ab,2); ab = reshape(ab,nrows*ncols,2); nColors = 1; [cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ... 'Replicates',1); %[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3); % Label every pixel in tha image using results from K means pixel_labels = reshape(cluster_idx,nrows,ncols); %figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');

% Create a blank cell array to store the results of clustering segmented_images = cell(1,3); % Create RGB label using pixel_labels rgb_label = repmat(pixel_labels,[1,1,3]);

for k = 1:nColors colors = I; colors(rgb_label ~= k) = 0; segmented_images{k} = colors; end

% figure, imshow(segmented_images{1});title('Objects in Cluster 1');

%figure, imshow(segmented_images{2});title('Objects in Cluster 2');

seg_img = im2bw(segmented_images{1}); figure, imshow(seg_img);title('Segmented Tumor'); %seg_img = img; % Extract features using DWT x = double(seg_img); m = size(seg_img,1); n = size(seg_img,2); %signal1 = (rand(m,1)); %winsize = floor(size(x,1)); %winsize = int32(floor(size(x))); %wininc = int32(10); %J = int32(floor(log(size(x,1))/log(2))); %Features = getmswpfeat(signal,winsize,wininc,J,'matlab');

%m = size(img,1); %signal = rand(m,1); signa1 = seg_img(:,:); %Feat = getmswpfeat(signal,winsize,wininc,J,'matlab'); %Features = getmswpfeat(signal,winsize,wininc,J,'matlab');

[cA1,cH1,cV1,cD1] = dwt2(signal,'db4'); [cA2,cH2,cV2,cD2] = dwt2(cA1,'db4'); [cA3,cH3,cV3,cD3] = dwt2(cA2,'db4');

DWT_feat = [cA3,cH3,cV3,cD3]; G = pca(DWT_feat); whos DWT_feat whos G g = graycomatrix(G); stats = graycoprops(g,'Contrast Correlation Energy Homogeneity'); Contrast = stats.Contrast; Correlation = stats.Correlation; Energy = stats.Energy; Homogeneity = stats.Homogeneity; Mean = mean2(G); Standard_Deviation = std2(G); Entropy = entropy(G); RMS = mean2(rms(G)); %Skewness = skewness(img) Variance = mean2(var(double(G))); a = sum(double(G(:))); Smoothness = 1-(1/(1+a)); Kurtosis = kurtosis(double(G(:))); Skewness = skewness(double(G(:))); % Inverse Difference Movement m = size(G,1); n = size(G,2); in_diff = 0; for i = 1:m for j = 1:n temp = G(i,j)./(1+(i-j).^2); in_diff = in_diff+temp; end end IDM = double(in_diff);

feat = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM];

% Normalize features to have zero mean and unit variance %feat = real(feat); %feat = (feat-mean(feat(:))); %feat=feat/std(feat(:)); %DWT_Features = cell2mat(DWT_feat); %mean = mean(DWT_feat(:));

%feat1 = getmswpfeat(signal1,20,2,2,'matlab');

%signal2 = rand(n,1); %feat2 = getmswpfeat(signal2,200,6,2,'matlab');

%feat2 = getmswpfeat(signal2,20,2,2,'matlab');

% Combine features %features = [feat1;feat2];

% Apply PCA to reduce dimensionality %coeff = pca(features);

% Check dimensionality reduction %whos features %whos coeff

load Trainset.mat xdata = meas; group = label; %svmStruct = svmtrain(xdata,group,'showplot',false); % species = svmclassify(svmStruct,feat) svmStruct1 = svmtrain(xdata,group,'kernel_function', 'linear'); %cp = classperf(group); %feat1 = [0.1889 0.9646 0.4969 0.9588 31.3445 53.4054 3.0882 6.0023 1.2971e+03 1.0000 4.3694 1.5752 255]; % feat2 = [ 0.2790 0.9792 0.4229 0.9764 64.4934 88.6850 3.6704 8.4548 2.3192e+03 1.0000 1.8148 0.7854 255]; species = svmclassify(svmStruct1,feat,'showplot',false) %classperf(cp,species,feat2); %classperf(cp,feat2); % Accuracy = cp.CorrectRate; % Accuracy = Accuracy*100

% Polynomial Kernel % svmStruct2 = svmtrain(xdata,group,'Polyorder',2,'Kernel_Function','polynomial'); %species_Poly = svmclassify(svmStruct2,feat,'showplot',false)

% Quadratic Kernel %svmStruct3 = svmtrain(xdata,group,'Kernel_Function','quadratic'); %species_Quad = svmclassify(svmStruct3,feat,'showplot',false)

% RBF Kernel %svmStruct4 = svmtrain(xdata,group,'RBF_Sigma', 3,'Kernel_Function','rbf','boxconstraint',Inf); %species_RBF = svmclassify(svmStruct4,feat,'showplot',false)

% To plot classification graphs, SVM can take only two dimensional data data1 = [meas(:,1), meas(:,2)]; newfeat = [feat(:,1),feat(:,2)];

pause %close all

svmStruct1_new = svmtrain(data1,group,'kernel_function', 'linear','showplot',false); species_Linear_new = svmStruct1_new(newfeat,'showplot',false);

%% % Multiple runs for accuracy highest is 90% load Trainset.mat %data = [meas(:,1), meas(:,2)]; data = meas; groups = ismember(label,'BENIGN '); groups = ismember(label,'MALIGNANT'); [train,test] = crossvalind('HoldOut',groups); cp = classperf(groups); %svmStruct = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf'); svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear'); classes = svmclassify(svmStruct,data(test,:),'showplot',false); classperf(cp,classes,test); Accuracy_Classification = cp.CorrectRate.*100; sprintf('Accuracy of Linear kernel is: %g%%',Accuracy_Classification)

%% Accuracy with RBF svmStruct_RBF = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf'); classes2 = svmclassify(svmStruct_RBF,data(test,:),'showplot',false); classperf(cp,classes2,test); Accuracy_Classification_RBF = cp.CorrectRate.*100; sprintf('Accuracy of RBF kernel is: %g%%',Accuracy_Classification_RBF)

%% Accuracy with Polynomial svmStruct_Poly = svmtrain(data(train,:),groups(train),'Polyorder',2,'Kernel_Function','polynomial'); classes3 = svmclassify(svmStruct_Poly,data(test,:),'showplot',false); classperf(cp,classes3,test); Accuracy_Classification_Poly = cp.CorrectRate.*100; sprintf('Accuracy of Polynomial kernel is: %g%%',Accuracy_Classification_Poly)

%%

% 5 fold cross validation % 5 fold cross validation load Normalized_Features.mat xdata = norm_feat; group = norm_label; indicies = crossvalind('Kfold',label,5); cp = classperf(label); for i = 1:length(label) test = (indicies==i);train = ~ test; svmStruct = svmtrain(xdata(train,:),group(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf'); classes = svmclassify(svmStruct,xdata(test,:),'showplot',false); %class = svmclassify(meas(test,:),meas(train,:),label(train,:)); classperf(cp,classes,test); end %Accu = cp.ClassifiedRate; Accuracy = cp.CorrectRate; %sprintf('Accuracy of classification with 5 fold cross validation is: %g%%',Accu*100)

%% Accuracy for normalized features %load Normalized_Features.mat % xdata = norm_feat; % data = [xdata(:,1), xdata(:,2)];

%groups = ismember(label,'BENIGN '); %groups = ismember(label,'MALIGNANT'); %[train,test] = crossvalind('HoldOut',groups); %cp = classperf(groups); %svmStruct = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf'); %svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear'); %classes = svmclassify(svmStruct,data(test,:),'showplot',false); %classperf(cp,classes,test); %Accuracy_New = cp.CorrectRate.*100; %sprintf('Accuracy of classification is: %g%%',Accuracy_New); %% Hold out on normalized features highest is 70% %load Normalized_Features.mat % xdata = norm_feat; %data = norm_feat; % group = norm_label; % groups = ismember(label,'BENIGN '); %groups = ismember(label,'MALIGNANT'); %[train,test] = crossvalind('HoldOut',groups); %cp = classperf(groups); %svmStruct = svmtrain(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf'); %svmStruct = svmtrain(data(train,:),groups(train),'showplot',false,'kernel_function','linear'); %classes = svmclassify(svmStruct,data(test,:),'showplot',false); %classperf(cp,classes,test); %Accuracy_Classification = cp.CorrectRate.*100; %sprintf('Accuracy of classification is: %g%%',Accuracy_Classification) rng(1); % For reproducibility r = sqrt(rand(100,1)); % Radius t = 2*pi*rand(100,1); % Angle data1 = [r.*cos(t), r.*sin(t)]; % Points r2 = sqrt(3*rand(100,1)+1); % Radius figure; plot(data1(:,1),data1(:,2),'r.','MarkerSize',15) hold on plot(data2(:,1),data2(:,2),'b.','MarkerSize',15) ezpolar(@(x)1);ezpolar(@(x)2); axis equal hold off t2 = 2*pi*rand(100,1); % Angle data2 = [r2.*cos(t2), r2.*sin(t2)]; % points data3 = [data1;data2]; %Train the SVM Classifier cl = fitcsvm(data3,theclass,'KernelFunction','rbf',... 'BoxConstraint',Inf,'ClassNames',[-1,1]);

% Predict scores over the grid d = 0.02; [x1Grid,x2Grid] = meshgrid(min(data3(:,1)):d:max(data3(:,1)),... min(data3(:,2)):d:max(data3(:,2))); xGrid = [x1Grid(:),x2Grid(:)]; [~,scores] = predict(cl,xGrid) ;

% Plot the data and the decision boundary figure; h(1:2) = gscatter(data3(:,1),data3(:,2),theclass,'rb','.'); hold on ezpolar(@(x)1); h(3) = plot(data3(cl.IsSupportVector,1),data3(cl.IsSupportVector,2),'ko'); contour(x1Grid,x2Grid,reshape(scores(:,2),size(x1Grid)),[0 0],'k'); legend(h,{'-1','+1','Support Vectors'}); axis equal hold off theclass = ones(200,1); theclass(1:100) = -1;