MATLAB: Automated detection of diabetic retinopathy

Question

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

Best Answer

Hello Anas Bilal,

Code tested working on 2012.

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syms i1 i2 i3 i4 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11;

i1 i2 i3 i4 start out as symbolic

    eq1 = 3-V(j,1)*(i1)-V(j,2)*(i1-i4)-V(j,3)*(i1+i2)-V(j,4)*i1==0;
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and get used in those equations, so those start out as symbolic equations

    i1=0;
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Answer 1

Hello Anas Bilal,

Code tested working on 2012.