machine learning for medical analysis

2 次查看（过去 30 天）
Mohamed Nasr
Mohamed Nasr 2020-8-23
评论： Mohamed Nasr 2020-8-23
close all
clc
clear all
[filename,pathname] = uigetfile({'*.*';'*.bmp';'*.tif';'*.gif';'*.png'},'D:\matlab aml\dataset3');
I = imread([pathname,filename]);
figure, imshow(I); title('Brain MRI Image');
I = imresize(I,[200,200]);
% Convert to grayscale
gray = rgb2gray(I);
% Binarization for segmentation
level = graythresh(I);
img = im2bw(I,level);
figure, imshow(img);title(' 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');
% 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);
signal1 = seg_img(:,:);
%Feat = getmswpfeat(signal,winsize,wininc,J,'matlab');
%Features = getmswpfeat(signal,winsize,wininc,J,'matlab');
[cA1,cH1,cV1,cD1] = dwt2(signal1,'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
a = magic(4);
b = ones(2, 4) * -5.7;
c = [8 6 4 2];
save -ascii train.m a b c
clear a b c
load train.m -ascii
xdata = meas();
group = label;
%svmStruct = svmtrain(xdata,group,'showplot',false);
% species = svmclassify(svmStruct,feat)
svmStruct1 = fitcsvm(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 = ClassificationSVM(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 = fitcsum(data1,group,'kernel_function', 'linear','showplot',false);
species_Linear_new = ClassificationSVM(svmStruct1_new,newfeat,'showplot',false);
% Multiple runs for accuracy highest is 90%
%load Trainset.mat
a = magic(4);
b = ones(2, 4) * -5.7;
c = [8 6 4 2];
save -ascii train.m a b c
clear a b c
load train.m -ascii
%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 = fitcsvm(data(train,:),groups(train),'showplot',false,'kernel_function','linear');
classes = ClassificationSVM(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 = fitcsvm(data(train,:),groups(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
classes2 = ClassificationSVM(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 = fitcsvm(data(train,:),groups(train),'Polyorder',2,'Kernel_Function','polynomial');
classes3 = ClassificationSVM(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 = fitcsvm(xdata(train,:),group(train),'boxconstraint',Inf,'showplot',false,'kernel_function','rbf');
classes = ClassificationSVM(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)
this is my code and my error in meas function what is the problem
  4 个评论
显示 2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Image Segmentation and Analysis 的更多信息

标签

产品

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by