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covid_imge_ayiklama_type2.m
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covid_imge_ayiklama_type2.m
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clear all;
close all;
Base = '..\covid19_ECG\ECG Images of COVID-19 Patients (250)\type_2';
List = dir(fullfile(Base, '**', '*.jpg'));
Files = fullfile({List.folder}, {List.name});
load('coordinates.mat');
covid_statistical=[];
%iFile = 1
% figure, set(gcf,'visible','off');
for iFile = 1:numel(Files)
I = imread(Files{iFile});
%imshow(I)
%kýrpma
% [J,rect] = imcrop(I); %koordinatlarý bulmak icin
%[103.5 104.5 723 432] tüm ecg
% imshow(I2)
%I3 = imcrop(I2,[40.5 4.5 152 90]); % bir kanal
% imshow(I_image)
%different sizes :(
size_I=size (I);
if (size_I(1)<950)
I2 = imcrop(I,[38.5 195.5 1105 662]);
%%%%[J,rect] = imcrop(I2); %koordinatlarý bulmak icin
I_image= imcrop(I2,[63.5 0.5 189 189]);
aVR_image= imcrop(I2,[360.5 0.5 189 189]); % imshow(aVR_image)
V1_image=imcrop(I2,[593.5 0.5 189 189]);
V4_image= imcrop(I2,[871.5 0.5 189 189]);
II_image= imcrop(I2,[63.5 189.5 189 189]);
aVL_image= imcrop(I2,[360.5 189.5 189 189]);
V2_image=imcrop(I2,[593.5 189.5 189 189]);
V5_image= imcrop(I2,[871.5 189.5 189 189]);
III_image= imcrop(I2,[63.5 378.5 189 189]);
aVF_image= imcrop(I2,[360.5 378.5 189 189]);
V3_image=imcrop(I2,[593.5 378.5 189 189]);
V6_image= imcrop(I2,[871.5 378.5 189 189]);
else %normal or stisfied size
I2 = imcrop(I,[41.5 218.5 1230 733]);
I_image= imcrop(I2,[72.5 0.5 189 189]);
aVR_image= imcrop(I2,[402.5 0.5 189 189]); % imshow(aVR_image)
V1_image=imcrop(I2,[685.5 0.5 189 189]);
V4_image= imcrop(I2,[990 0.5 189 189]);
II_image= imcrop(I2,[72.5 189.5 189 189]);
aVL_image= imcrop(I2,[402.5 189.5 189 189]);
V2_image=imcrop(I2,[685.5 189.5 189 189]);
V5_image= imcrop(I2,[990 189.5 189 189]);
III_image= imcrop(I2,[72.5 378.5 189 189]);
aVF_image= imcrop(I2,[402.5 378.5 189 189]);
V3_image=imcrop(I2,[685.5 378.5 189 189]);
V6_image= imcrop(I2,[990 378.5 189 189]);
end
% -li sinyal oluþturma
I_image_neg=I_image(end:-1:1,:,:); %figure, imshow(I_image_neg)
aVR_image_neg=aVR_image(end:-1:1,:,:);
II_image_neg=II_image(end:-1:1,:,:);
aVL_image_neg=aVL_image(end:-1:1,:,:);
III_image_neg=III_image(end:-1:1,:,:);
aVF_image_neg=aVF_image(end:-1:1,:,:);
all_cropped_image=cat(4, I_image, aVL_image, III_image_neg, aVF_image_neg, ...
II_image_neg, aVR_image, I_image_neg, aVL_image_neg, III_image, aVF_image,...
II_image, aVR_image_neg, V1_image, V2_image, V3_image, V4_image, V5_image, V6_image );
%size(all_cropped_image)
% figure, imshow(all_cropped_image(:,:,:,5));
% coordinate_labels=["I", "aVL" , "III(-)" ,"aVF(-)" ,"II(-)" ,"aVR", "I(-)" , "aVL(-)", ...
%"III", "aVF", "II", "aVR(-)", "V1", "V2", "V3", "V4", "V5", "V6"];
%adjust
for i=1:18
K = imadjust(all_cropped_image(:,:,:,i),[0.1 0.7],[]);
% figure
% imshow(K)
% arkaplan kaldýrma
binaryImage = K(:, :, 2) < 250; % Or whatever threshold works.
binaryImage = bwareafilt(binaryImage, 1); % Extract only the largest blob.
% figure, imshow(1-binaryImage)
all_cropped_image_binary(:,:,i)=(binaryImage);
% %figure, imshow (all_cropped_image_binary(:,:,5))
% switch (i)
% case 1
% channel='\I\';
% case 11
% channel='\II\';
% case 9
% channel='\III\';
% case 6
% channel='\aVR\';
% case 2
% channel='\avL\';
% case 10
% channel='\avF\';
% case 13
% channel='\V1\';
% case 14
% channel='\V2\';
% case 15
% channel='\V3\';
% case 16
% channel='\V4\';
% case 17
% channel='\V5\';
% case 18
% channel='\V6\';
% case 7
% channel='\I(-)\';
% case 5
% channel='\II(-)\';
% case 3
% channel='\III(-)\';
% case 12
% channel='\aVR(-)\';
% case 8
% channel='\avL(-)\';
% case 4
% channel='\avF(-)\';
%
% end
%
% % coordinate_labels=["I", "aVL" , "III(-)" ,"aVF(-)" ,"II(-)" ,"aVR", "I(-)" , "aVL(-)", ...
% %"III", "aVF", "II", "aVR(-)", "V1", "V2", "V3", "V4", "V5", "V6"];
%
% %%save ECG images
% kayit_yeri=strcat( '..\covid19_ECG\preprocessed_dataset\covid_19'...
% ,channel);
% kayit_yeri=strcat(kayit_yeri,num2str(iFile+89)); %continue on type1
% kayit_yeri=strcat(kayit_yeri,'.png');
% imshow (1-all_cropped_image_binary(:,:,i))
% export_fig( kayit_yeri ,'-transparent', '-r300')
% %-m2.5
%
end % 12 channel
% %comatrix
% comat=[];
% for k=1:18
% comat= [comat graycomatrix(logical(all_cropped_image_binary(:,:,k)))];
%
% end
%asýl feature cikarma burasi, eskiden matrix alýyorduk þimdi burada herþeyi
%düzgünce hesaplýyoruz
comat_energy=[];
% comat_correlation=[];
% comat_contrast=[];
% comat_homogeneity=[];
for k=1:18
glcms=graycomatrix(logical(all_cropped_image_binary(:,:,k)));
stats = graycoprops(glcms);% Calculate properties of gray-level co-occurrence matrix
comat_energy= [comat_energy stats.Energy];
% comat_correlation= [comat_correlation stats.Correlation];
% comat_contrast= [comat_contrast stats.Contrast];
% comat_homogeneity= [comat_homogeneity stats.Homogeneity];
end %feature exraction
%statistical difference
% covid_statistical= [covid_statistical; comat_energy' comat_correlation' comat_contrast' comat_homogeneity'];
% statistical_label= ["Energy","Correlation","Contrast","Homogeneity"] ;
%loksayona göre haritalama
% % x_coordinates = [2.5; 1.5; 3; 2; 1; 2; 3; 5; 6; 7; 6; 5; 6.5; 5.5];
% % y_coordinates = [7; 5.7; 5.8; 5; 4; 2; 1; 1; 2; 4; 5; 5.8; 6; 7];
% xi=linspace(min(x_coordinates),max(x_coordinates),100);
% yi=linspace(min(y_coordinates),max(y_coordinates),100);
% [XI YI]=meshgrid(xi,yi);
% ZI = griddata(x_coordinates,y_coordinates,comat_energy(1,1:end)',XI,YI,'natural');
%
% figure, set(gcf,'visible','off');
% contourf(XI,YI,ZI,50,'edgecolor','none');
% % colormap(jet);
% axis off
% set(gcf,'position',[-15,15,710,720])
% set(gca,'LooseInset',get(gca,'TightInset'))
%
% %save features map
% kayit_yeri=strcat( '..\covid19_ECG\feature_maps\covid_19\'...
% ,num2str(iFile+89));
% kayit_yeri=strcat(kayit_yeri,'.png');
% export_fig( kayit_yeri ,'-transparent') %, '-r300'
% %-m2.5
end% dosya