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Automated detection of diabetic retinopathy

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Can any oner please help me to solve this error
clc
clear all
close all
cd Database
DF=[]
for i=1:36
%st=int2str(i)
%str=strcat(st,'.jpg');
I=imread('image001.png');
I=I(:,:,2);
I=imresize(I,[200 200]);
L=double(I);
% % lapale
sp=fspecial('laplacian');
M=imfilter(I,sp);
% to find average gradient Q
Q=mean(mean(M));
t=191.25;
T=M>t;
BW = bwareaopen(T,2);
N=BW;
[m n]=size(N);
ed=[];
tex=[];
p=0;
q=0;
for i=1:m
for j=1:n
if N(i,j)==0
p=p+1;
tex(p)=I(i,j);
else
q=q+1;
ed(q)=M(i,j);
end
end
end
Med=mean(ed(:));
Mtex=mean(tex(:));
v1=(Med-Q)/Med;
v2=(Q-Mtex)/Q;
% to find v
v = order7(M,t,v1,v2);
%convolve with the mask
S=padarray(L,[2,2]);
[m n]=size(S);
J=zeros(size(S));
for i1=1:m-4
for j1=1:n-4
F2= [ v(i1,j1)/(8*(v(i1,j1)-2)) 0 v(i1,j1)/(8*(v(i1,j1)-2)) 0 v(i1,j1)/(8*(v(i1,j1)-2));
0 -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) 0;
v(i1,j1)/(8*(v(i1,j1)-2)) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) 8/(8-12*v(i1,j1)+4*v(i1,j1)^2) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) v(i1,j1)/(8*(v(i1,j1)-2));
0 -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) 0;
v(i1,j1)/(8*(v(i1,j1)-2)) 0 v(i1,j1)/(8*(v(i1,j1)-2)) 0 v(i1,j1)/(8*(v(i1,j1)-2))];
J(i1,j1)=sum(sum((F2).*S(i1:i1+4,j1:j1+4)));
end
end
%% prepro
g=fspecial('gaussian');
pre=imfilter(double(J),g);
% % thresholding
gr=uint8(pre);
th=graythresh(gr);
be=gr>140;
%figure,imshow(be)
gchanel=uint8(pre); %Green Chanel Extraction
Igchanel = imcomplement(gchanel); %Inversion
conenhance = adapthisteq(Igchanel); %Contrast Enhancement
gg=fspecial('gaussian',2)
g = imfilter(conenhance,gg); %Gaussian filtering
se = strel('ball',8,8) ;
tophat = imtophat(g,se); %Tophat transform
med = medfilt2(tophat); %Median filtering
background = imopen(med,strel('disk',15));
I2 = med - background; % Background Removal
I3 = imadjust(I2);%Intensity Adjustment
level = graythresh( gchanel); % Gray Threshold
bw = imbinarize(I3,level);
se=strel('disk',2);
di=imdilate(bw,se);
se=strel('disk',4)
er=imerode(di,se);
post=bwareaopen(bw,8);
re=imresize(bw,[200 200]);
outt=immultiply(I,imcomplement(re));
% %figure,imshow(outt)
% % FEATURES
vessel=outt;
I2=vessel;
m=size(I2,1);
n=size(I2,2);
for di=2:m-1
for dj=2:n-1
J10=I2(di,dj);
I3(di-1,dj-1)=I2(di-1,dj-1)>J10;
I3(di-1,dj)=I2(di-1,dj)>J10;
I3(di-1,dj+1)=I2(di-1,dj+1)>J10;
I3(di,dj+1)=I2(di,dj+1)>J10;
I3(di+1,dj+1)=I2(di+1,dj+1)>J10;
I3(di+1,dj)=I2(di+1,dj)>J10;
I3(di+1,dj-1)=I2(di+1,dj-1)>J10;
I3(di,dj-1)=I2(di,dj-1)>J10;
LBP(di,dj)=I3(di-1,dj-1)*2^7+I3(di-1,dj)*2^6+I3(di-1,dj+1)*2^5+I3(di,dj+1)*2^4+I3(di+1,dj+1)*2^3+I3(di+1,dj)*2^2+I3(di+1,dj-1)*2^1+I3(di,dj-1)*2^0;
end
end
% % glcm
glcms = graycomatrix(outt);
stats = graycoprops(glcms,'Energy', 'Homogeneity');
en=stats.Energy;
corre=stats.Homogeneity;
% % histogram
[hi]=imhist(bw);
maxi=max(hi);
mini=min(hi);
med=median(hi);
featureall=[corre en sum(sum(LBP))/(m*n)];
DF=[DF;featureall]
end
cd ..
for i=1:1
b=input('ENTER : ')
I= imread(b);
I=I(:,:,2);
I=imresize(I,[200 200]);
L=double(I);
% % lapale
sp=fspecial('laplacian');
M=imfilter(I,sp);
% to find average gradient Q
Q=mean(mean(M));
t=191.25;
T=M>t;
BW = bwareaopen(T,2);
N=BW;
[m n]=size(N);
ed=[];
tex=[];
p=0;
q=0;
for i=1:m
for j=1:n
if N(i,j)==0
p=p+1;
tex(p)=I(i,j);
else
q=q+1;
ed(q)=M(i,j);
end
end
end
Med=mean(ed(:));
Mtex=mean(tex(:));
v1=(Med-Q)/Med;
v2=(Q-Mtex)/Q;
% to find v
v = order7(M,t,v1,v2);
%convolve with the mask
S=padarray(L,[2,2]);
[m n]=size(S);
J=zeros(size(S));
for i1=1:m-4
for j1=1:n-4
F2= [ v(i1,j1)/(8*(v(i1,j1)-2)) 0 v(i1,j1)/(8*(v(i1,j1)-2)) 0 v(i1,j1)/(8*(v(i1,j1)-2));
0 -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) 0;
v(i1,j1)/(8*(v(i1,j1)-2)) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) 8/(8-12*v(i1,j1)+4*v(i1,j1)^2) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) v(i1,j1)/(8*(v(i1,j1)-2));
0 -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) -(v(i1,j1)/(4*(v(i1,j1)^2-3*v(i1,j1)+2))) 0;
v(i1,j1)/(8*(v(i1,j1)-2)) 0 v(i1,j1)/(8*(v(i1,j1)-2)) 0 v(i1,j1)/(8*(v(i1,j1)-2))];
J(i1,j1)=sum(sum((F2).*S(i1:i1+4,j1:j1+4)));
end
end
%figure,imshow(uint8(J))
title('enhaned Image');
%% prepro
g=fspecial('gaussian');
pre=imfilter(double(J),g);
%figure,imshow(uint8(pre),[]);
title('preprocess');
% % thresholding
gr=uint8(pre);
th=graythresh(gr);
be=gr>140;
%figure,imshow(be,[])
gchanel=uint8(pre); %Green Chanel Extraction
Igchanel = imcomplement(gchanel); %Inversion
conenhance = adapthisteq(Igchanel); %Contrast Enhancement
gg=fspecial('gaussian',2)
g = imfilter(conenhance,gg); %Gaussian filtering
se = strel('ball',8,8) ;
tophat = imtophat(g,se); %Tophat transform
med = medfilt2(tophat); %Median filtering
background = imopen(med,strel('disk',15));
I2 = med - background; % Background Removal
I3 = imadjust(I2);%Intensity Adjustment
level = graythresh( gchanel); % Gray Threshold
bw = imbinarize(I3,level);
se=strel('disk',2)
di=imdilate(bw,se);
se=strel('disk',4)
er=imerode(di,se);
post=bwareaopen(bw,8);
re=imresize(bw,[200 200]);
outt=immultiply(I,imcomplement(re));
% %figure,imshow(outt)
% % FEATURES
vessel=outt;
I2=vessel;
m=size(I2,1);
n=size(I2,2);
for di=2:m-1
for dj=2:n-1
J10=I2(di,dj);
I3(di-1,dj-1)=I2(di-1,dj-1)>J10;
I3(di-1,dj)=I2(di-1,dj)>J10;
I3(di-1,dj+1)=I2(di-1,dj+1)>J10;
I3(di,dj+1)=I2(di,dj+1)>J10;
I3(di+1,dj+1)=I2(di+1,dj+1)>J10;
I3(di+1,dj)=I2(di+1,dj)>J10;
I3(di+1,dj-1)=I2(di+1,dj-1)>J10;
I3(di,dj-1)=I2(di,dj-1)>J10;
LBP(di,dj)=I3(di-1,dj-1)*2^7+I3(di-1,dj)*2^6+I3(di-1,dj+1)*2^5+I3(di,dj+1)*2^4+I3(di+1,dj+1)*2^3+I3(di+1,dj)*2^2+I3(di+1,dj-1)*2^1+I3(di,dj-1)*2^0;
end
end
% % glcm
glcms = graycomatrix(outt);
stats = graycoprops(glcms,'Energy', 'Homogeneity');
en=stats.Energy;
corre=stats.Homogeneity;
% % histogram
[hi]=imhist(outt);
maxi=max(hi);
mini=min(hi);
med=median(hi);
QF=[corre en sum(sum(LBP))/(m*n)];
% % feature ranking
%% % % % % % % % MULTI SVM classification
train=DF;
xdata =train;
TrainingSet=xdata
TestSet=QF;
GroupTrain=[1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;2;2;2;2;2;2;2;2;2;3;3;3;3;3;3]
u=unique(GroupTrain);
numClasses=length(u);
result = zeros(length(TestSet(:,1)),1);
%build models
for k=1:numClasses
%Vectorized statement that binarizes Group
%where 1 is the current class and 0 is all other classes
G1vAll=(GroupTrain==u(k));
models(k) = svmtrain(TrainingSet,G1vAll);
end
%classify test cases
for j=1:size(TestSet,1)
for k=1:numClasses
if(svmclassify(models(k),TestSet(j,:)))
break;
end
end
result(j) = k;
end
%figure,
subplot(3,3,1)
imshow(I)
title('input')
subplot(3,3,2)
imshow(uint8(pre))
title('preprocess')
subplot(3,3,3)
imshow(be)
title('disk')
subplot(3,3,4)
imshow(outt)
title('vessel')
subplot(3,3,5)
imshow(uint8(LBP),[])
title('LBP')
pause(1)
if result==1
msgbox('NORMAL')
elseif result==2
msgbox('DR')
elseif result==3
msgbox('AMD')
end
end
% COMPARE
sumval_svm=[23 24 26 27 28 ]
sumval_orig=[30 ]
for ii=1:length(sumval_svm)
diff_tree=sumval_svm(ii)-sumval_orig;
locv=find(diff_tree);
% if(isempty(locv))
True_positive=sum(sumval_orig);
False_positive=abs(1-sum(abs(diff_tree)));
% else
% True_positive=sum(sumval_svm);
True_negative=(sum(sumval_svm));
locb=find(diff_tree>0);
False_negative=1-sum(diff_tree(locb));
% end
accuracy2(ii) = sumval_svm(ii)/sumval_orig;
sensitivity2(ii)=True_positive/(True_positive+False_positive)
specificity2(ii)=True_negative/(True_negative+False_positive)
end
% COMPARE
sumval_svm=[19 20 23 24 26 ]
sumval_orig=[30 ]
for ii=1:length(sumval_svm)
diff_tree=sumval_svm(ii)-sumval_orig;
locv=find(diff_tree);
% if(isempty(locv))
True_positive=sum(sumval_orig);
False_positive=abs(1-sum(abs(diff_tree)));
% else
% True_positive=sum(sumval_svm);
True_negative=(sum(sumval_svm));
locb=find(diff_tree>0);
False_negative=1-sum(diff_tree(locb));
% end
accuracy3(ii) = sumval_svm(ii)/sumval_orig;
sensitivity3(ii)=True_positive/(True_positive+False_positive)
specificity3(ii)=True_negative/(True_negative+False_positive)
end
axis([1 5 0 1])
%figure,
plot(accuracy2','r-*','LineWidth',2),hold on
plot(accuracy3','k-*','LineWidth',2),hold on
grid on
axis([1 5 0 1])
legend('acc-svm','acc-nn')
xlabel('Trails ')
ylabel('claasification result')
grid on
title('performance')
%figure,
plot(sensitivity2','c-*','LineWidth',2),hold on
plot(sensitivity3','m-*','LineWidth',2),hold on
grid on
axis([1 5 0 1])
legend('sen-svm','sen-nn')
xlabel('Trails ')
ylabel('claasification result')
grid on
title('performance')
%figure,
plot(specificity2-0.1','b-*','LineWidth',2),hold on
plot(specificity3-0.1','k-*','LineWidth',2),hold on
grid on
axis([1 5 0 1])
legend('spec-svm','spec-nn')
xlabel('Trails ')
ylabel('claasification result')
grid on
title('performance')

  2 Comments

Mrutyunjaya Hiremath
Mrutyunjaya Hiremath on 24 Apr 2020
Before comimg to 'svmtrain'.
some lines of code is missing.
What is this Function 'order7'?
v = order7(M,t,v1,v2);
Anas Bilal
Anas Bilal on 25 Apr 2020
this is the function order 7
function [v ] = order7(M,t,v1,v2)
M=double(M);
[m n]=size(M);
for i=1:m
for j=1:n
if M(i,j)>=t && ((M(i,j)-t)/M(i,j))>=v1
v(i,j)=(M(i,j)-t)/M(i,j);
elseif M(i,j)>=t && ((M(i,j)-t)/M(i,j))<v1
v(i,j)=v1;
elseif M(i,j)>2 && M(i,j)<t && (M(i,j)/t)>=v2
v(i,j)=v2;
elseif M(i,j)>2 && M(i,j)<t && (M(i,j)/t)<v2
v(i,j)=(M(i,j)/t);
else 0<=M(i,j)<=2
v(i,j)=0;
end
end
end
end

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Accepted Answer

Mrutyunjaya Hiremath
Mrutyunjaya Hiremath on 26 Apr 2020
Hello Anas Bilal,
Code tested working on 2012.

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