gusucode.com > Diabetic retinopathy > FundsImages.m
function varargout = FundsImages(varargin)
% FUNDSIMAGES MATLAB code for FundsImages.fig
% FUNDSIMAGES, by itself, creates a new FUNDSIMAGES or raises the existing
% singleton*.
%
% H = FUNDSIMAGES returns the handle to a new FUNDSIMAGES or the handle to
% the existing singleton*.
%
% FUNDSIMAGES('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in FUNDSIMAGES.M with the given input arguments.
%
% FUNDSIMAGES('Property','Value',...) creates a new FUNDSIMAGES or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before FundsImages_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to FundsImages_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help FundsImages
% Last Modified by GUIDE v2.5 23-Jul-2019 01:30:47
% Begin initialization code - DO NOT EDIT
global ma;
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @FundsImages_OpeningFcn, ...
'gui_OutputFcn', @FundsImages_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before FundsImages is made visible.
function FundsImages_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to FundsImages (see VARARGIN)
% Choose default command line output for FundsImages
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes FundsImages wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = FundsImages_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in loadimage.
function loadimage_Callback(hObject, eventdata, handles)
% hObject handle to loadimage (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global fname;
[fn pn] = uigetfile('*.jpg','*.png','Select Image File');
complete = strcat(pn,fn);
fname = complete;
set(handles.inputtxt,'string',complete);
pic1=imread (complete);
imshow(pic1,'Parent',handles.inputimg);
title(handles.inputimg,'Fundus Images');
function inputtxt_Callback(hObject, eventdata, handles)
% hObject handle to inputtxt (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of inputtxt as text
% str2double(get(hObject,'String')) returns contents of inputtxt as a double
% --- Executes during object creation, after setting all properties.
function inputtxt_CreateFcn(hObject, eventdata, handles)
% hObject handle to inputtxt (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in edge.
function edge_Callback(hObject, eventdata, handles)
% hObject handle to edge (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
pa = get(handles.inputtxt,'String');
A=imread(pa);
B=rgb2gray(A);
C=double(B);
for i=1:size(C,1)-2
for j=1:size(C,2)-2
%Sobel mask for x-direction:
Gx=((2*C(i+2,j+1)+C(i+2,j)+C(i+2,j+2))-(2*C(i,j+1)+C(i,j)+C(i,j+2)));
%Sobel mask for y-direction:
Gy=((2*C(i+1,j+2)+C(i,j+2)+C(i+2,j+2))-(2*C(i+1,j)+C(i,j)+C(i+2,j)));
%The gradient of the image
%B(i,j)=abs(Gx)+abs(Gy);
B(i,j)=sqrt(Gx.^2+Gy.^2);
end
end
Thresh=10;
B=max(B,Thresh);
B(B==round(Thresh))=0;
B=uint8(B);
imshow(~B);
% --- Executes on button press in pprocess.
function pprocess_Callback(hObject, eventdata, handles)
% hObject handle to pprocess (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% check image
pa = get(handles.inputtxt,'String');
im=imread(pa);
te = rgb2gray(im);
imshow(te);
function bw=adaptivethreshold(IM,ws,C,tm)
%ADAPTIVETHRESHOLD An adaptive thresholding algorithm that seperates the
%foreground from the background with nonuniform illumination.
% bw=adaptivethreshold(IM,ws,C) outputs a binary image bw with the local
% threshold mean-C or median-C to the image IM.
% ws is the local window size.
% tm is 0 or 1, a switch between mean and median. tm=0 mean(default); tm=1 median.
%
% Contributed by Guanglei Xiong (xgl99@mails.tsinghua.edu.cn)
% at Tsinghua University, Beijing, China.
%
% For more information, please see
% http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
if (nargin<3)
error('You must provide the image IM, the window size ws, and C.');
elseif (nargin==3)
tm=0;
elseif (tm~=0 && tm~=1)
error('tm must be 0 or 1.');
end
IM=mat2gray(IM);
if tm==0
mIM=imfilter(IM,fspecial('average',ws),'replicate');
else
mIM=medfilt2(IM,[ws ws]);
end
sIM=mIM-IM-C;
bw=im2bw(sIM,0);
bw=imcomplement(bw);
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Segmentation
pa = get(handles.inputtxt,'String');
im = imread(pa);
k =100;
img_hist = zeros(256,1);
hist_value = zeros(256,1);
for i=1:256
img_hist(i)=sum(sum(im==(i-1)));
end;
for i=1:256
hist_value(i)=i-1;
end;
%cluster initialization
cluster = zeros(k,1);
cluster_count = zeros(k,1);
for i=1:k
cluster(i)=uint8(rand*255);
end;
old = zeros(k,1);
while (sum(sum(abs(old-cluster))) >k)
old = cluster;
closest_cluster = zeros(256,1);
min_distance = uint8(zeros(256,1));
min_distance = abs(hist_value-cluster(1));
%calculate the minimum distance to a cluster
for i=2:k
min_distance =min(min_distance, abs(hist_value-cluster(i)));
end;
%calculate the closest cluster
for i=1:k
closest_cluster(min_distance==(abs(hist_value-cluster(i)))) = i;
end;
%calculate the cluster count
for i=1:k
cluster_count(i) = sum(img_hist .*(closest_cluster==i));
end;
for i=1:k
if (cluster_count(i) == 0)
cluster(i) = uint8(rand*255);
else
cluster(i) = uint8(sum(img_hist(closest_cluster==i).*hist_value(closest_cluster==i))/cluster_count(i));
end;
end;
end;
imresult=uint8(zeros(size(im)));
for i=1:256
imresult(im==(i-1))=cluster(closest_cluster(i));
end;
clustersresult=uint8(zeros(size(im)));
for i=1:256
clustersresult(im==(i-1))=closest_cluster(i);
end;
imshow(imresult);
% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton5 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
pa = get(handles.inputtxt,'String');
I = imread(pa);
J = imadjust(I,stretchlim(I),[]);
t = rgb2gray(J);
imshow(t);
% --- Executes on button press in pointint.
function pointint_Callback(hObject, eventdata, handles)
% hObject handle to pointint (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
pa = get(handles.inputtxt,'String');
img1=imread(pa);
img1=rgb2gray(img1);
img2=im2bw(img1,graythresh(img1));
img2=~img2;
B = bwboundaries(img2);
img3=imread(pa);
imshow(img3)
hold on
length(B)
len =0;
for k = 1:length(B)
boundary = B{k};
len(k) = length(boundary);
end
a = max(len);
Y = len(len ~= a);
b = max(Y);
for k = 1:length(B)
boundary = B{k};
if(b ==length(boundary) || a ==length(boundary))
plot(boundary(:,2), boundary(:,1), '--w', 'LineWidth', 0.2)
end
end
% --- Executes on button press in motionpattern.
function motionpattern_Callback(hObject, eventdata, handles)
% hObject handle to motionpattern (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global fname;
global image_enhance;
temp = fname;
pa = get(handles.inputtxt,'String');
fname =pa;
method_set={'AR','MCE','MCEDRC','TW-CES','DRC-CES','SF-CES','TW-CES-BLK','DRC-CES-BLK','SF-CES-BLK'};
[mr mc]=size(method_set);
jpqm=zeros(1,mc);
cef=zeros(1,mc);
image='image18';
lamda=1.95;
alpha=0.98;
gamma_JM=1.95;
pivotx=0.5;
pivoty=0.5;
power1=1.8;
power2=0.8;
gamma=1.95;
thresh1=0.10;
thresh2=gamma;
betaDC=1.0;
sigma_thresh=15;
nIteration=1;
fsave=1;
org_I = imread(pa);
[height width depth]=size(org_I);
CI_ycbcr=RGB_YCbCr(org_I);
Y=CI_ycbcr(:,:,1);
maxDC=double(max(max(Y)));
Cb=CI_ycbcr(:,:,2);
Cr=CI_ycbcr(:,:,3);
dct_Y=blkproc(Y,[8,8],@dct2);
dct_Cb=blkproc(Cb,[8 8],@dct2);
dct_Cr=blkproc(Cr,[8 8],@dct2);
org_dct_CI_ycbcr(:,:,1)=dct_Y;
org_dct_CI_ycbcr(:,:,2)=dct_Cb;
org_dct_CI_ycbcr(:,:,3)=dct_Cr;
%M is the index for method
M = 4;
dct_CI_ycbcr=org_dct_CI_ycbcr;
method=char(method_set(M));
sprintf('Using %s',char(method))
ofname=['result/' sprintf(image) '_' sprintf(method)];
% sprintf('Output file= %s ',ofname)
for i=1:nIteration
gamma_ret=ones(height/8+2,width/8+2);
for l=1:height/8+2
for m=1:width/8+2
gamma_ret(l+1,m+1)=gamma;
end
end
for l=0:height/8-1
for m=0:width/8-1
Z=enhance_composite_dc_sigma(dct_CI_ycbcr(l*8+1:l*8+8,m*8+1:m*8+8,:),betaDC,maxDC,1);
enh_dct_Ycbcr(l*8+1:l*8+8,m*8+1:m*8+8,:)=Z;
end
end
dct_CI_ycbcr=enh_dct_Ycbcr;
end
enh_Y=blkproc(enh_dct_Ycbcr(:,:,1),[8,8],@idct2);
enh_Cb=blkproc(enh_dct_Ycbcr(:,:,2),[8,8],@idct2);
enh_Cr=blkproc(enh_dct_Ycbcr(:,:,3),[8,8],@idct2);
Y=enh_Y;
Cb=enh_Cb;
Cr=enh_Cr;
jpqm(M)=jpeg_quality_score(uint8(Y));
[h w]=size(enh_Y);
[h w]=size(enh_Cb);
[h w]=size(enh_Cr);
enh_CI=zeros(h,w,3);
enh_CI(:,:,1)=enh_Y(1:h,1:w);
enh_CI(:,:,2)=enh_Cb(1:h,1:w);
enh_CI(:,:,3)=enh_Cr(1:h,1:w);
out=YCbCr_RGB(enh_CI);
cef(M)=colourfulness_metric(uint8(out))/colourfulness_metric(uint8(org_I));
I = rgb2gray(uint8(out));
image_enhance = I;
Threshold = 10;
bloodVessels = VesselExtract(image_enhance, Threshold);
% figure;
% subplot(121);imshow(image_enhance);title('Input Image');
vessel=im2uint8(bloodVessels);
bw = bwareaopen(vessel,10);
se=strel('disk',4); % morphological dilation with structural element disk with dia 4
a=imdilate(bw,se);
bw=imresize(a,[400,400])
pa = get(handles.inputtxt,'String');
image = imread(pa);
image1=image(:,:,2);
image1=imresize(image1,[400,400]);% copying G image to process further, using this 3d to 2d
image4=im2bw(image1);
b=imsubtract(bw,image4);
c=imresize(b,[400,400]);
d=imresize(a,[400,400]);
d=im2uint8(d);
[lab num]=bwlabel(d); % making labels for all extracted objects
areas=[];
for i=1:num % concluding centroids for all extracted objects
dummy=lab==i;
[r c]=find(dummy==1);
areas(i,1)=i;
areas(i,2)=round(mean(r));
areas(i,3)=round(mean(c));
end
areas=sortrows(areas,3); % sorting all elements according to there areas
od_loco=[areas(1,2) areas(1,3)];
od_bw=lab==areas(1,1); % extracting Bw OD part
d = uint8(d);
image_od=d.* uint8(od_bw);
image_od=im2uint8(image_od);
image_od1=imresize(image_od,[400,400]);
d=imresize(d,[400,400]);
e=imsubtract(d,image_od1);
h=imresize(e,[400,400]);
setappdata(0,'evalue1',h);
vessel = imresize(vessel,[400,400]);
ma = imsubtract(vessel,h);
%ilam coe start%
image=imresize(org_I,[400,400]);
image=im2uint8(image);
image1=image(:,:,2);
image1=imresize(image1,[400,400]);% copying G image to process further, using this 3d to 2d
[M,N] = size(image1);
sa = 2.0;
rt = mim(image1,sa);
[tt1,e1,cmtx] = myThreshold(rt);
ms = 45;
mk = msk(image1,ms);
rt2 = 255*ones(M,N);
for i=1:M
for j=1:N
if rt(i,j)>=tt1 & mk(i,j)==255
rt2(i,j)=0;
end
end
end
J = im2bw(rt2);
J= ~J;
[Label,Num] = bwlabel(J);
Lmtx = zeros(Num+1,1);
for i=1:M
for j=1:N
Lmtx(double(Label(i,j))+1) = Lmtx(double(Label(i,j))+1) + 1;
end
end
sLmtx = sort(Lmtx);
cp = 950;
for i=1:M
for j=1:N
if (Lmtx(double(Label(i,j)+1)) > cp) & (Lmtx(double(Label(i,j)+1)) ~= sLmtx(Num+1,1))
J(i,j) = 0;
else
J(i,j) = 1;
end
end
end
for i=1:M
for j=1:N
if mk(i,j)==0
J(i,j)=1;
end
end
end
vessels=imresize(~J,[400,400]);
vessel=im2uint8(vessels);
bw = bwareaopen(vessel,10);
image2=imresize(image1,[400,400]);
image4=im2bw(image1);
b=imsubtract(image4,bw);
c=imresize(b,[400,400]);
image_dummy =c;
%ilam coe ends %
se=strel('disk',4); % morphological dilation with structural element disk with dia 4
a=imdilate(c,se);
d=imresize(a,[400,400]);
d=im2uint8(d);
imwrite(d,'dilate.jpg','quality',50);
% image_dummy =d;
% subplot(4,4,6)
% imshow(image_dummy);
% title('joining close objects together');
[lab num]=bwlabel(d); % making labels for all extracted objects
areas=[];
for i=1:num % concluding centroids for all extracted objects
dummy=lab==i;
[r c]=find(dummy==1);
areas(i,1)=i;
areas(i,2)=round(mean(r));
areas(i,3)=round(mean(c));
end
areas=sortrows(areas,3); % sorting all elements according to there areas
od_loco=[areas(1,2) areas(1,3)];
image_od_c=image; % making cross lines about OD position
image_od_c(od_loco(1)-2:od_loco(1),:,1:2)=0;
image_od_c(od_loco(1)-2:od_loco(1),:,3)=255;
image_od_c(:,od_loco(2)-2:od_loco(2),1:2)=0;
image_od_c(:,od_loco(2)-2:od_loco(2),3)=255;
image_od_c=imresize(image_od_c,[400,400]);
imwrite(image_od_c,'OD.jpg','quality',50);
% image_dummy =image_od_c;
% subplot(4,4,7)
% imshow(image_dummy)
% title('OD position located');
od_bw=lab==areas(1,1); % extracting Bw OD part
d = uint8(d);
image_od=d.* uint8(od_bw);
imwrite(image_od,'OD1.jpg','quality',50);
% image_dummy =image_od;
% subplot(4,4,8)
% imshow(image_dummy);
% title('extracted OD bw');
image_od=im2uint8(image_od);
image_od1=imresize(image_od,[400,400]);
d=imresize(d,[400,400]);
e=imsubtract(d,image_od1);
h=imresize(e,[400,400]);
imwrite(h,'lesions.jpg','quality',50);
% image_dummy =h;
% subplot(4,4,9)
% imshow(image_dummy);
% title('neglected OD for G image')
lesions_bw=e & (~image_od1);
lesions_bw=imresize(lesions_bw,[400,400]);
image_f=imresize(image,[400,400]);% taking position values for features
[r c]=find(lesions_bw==1);
for i=1:length(r) % applying blue color for detected feature in maing RGB image
image_f(r(i),c(i),1:2)=0;
image_f(r(i),c(i),3)=255;
end
% image_dummy =image_f;
% subplot(4,4,10)
% imshow(image_dummy)
% title('predicted feature')
imwrite(image_f,'last.jpg','quality',50);
[y1,x1,z1] = size(org_I);
final=imresize(image_f,[y1,x1]);
imshow(final)
% pic = image_enhance;
% black = 0;
% white = 0;
% X=0;
% cnt = 1;
% for mm = 1:size(pic,1)
% for nn = 1:size(pic,2)
% a = pic(mm,nn,1);
% if(a > 0 && a < 150)
% black = black + 1;
% pic(mm,nn, :) = a;
% elseif(a < 220)
% pic(mm,nn, :) = 120;
% else
% white = white + 1;
% pic(mm,nn, :) = 255;
% X(cnt,1) = nn;
% X(cnt,2) = mm;
% cnt = cnt + 1;
% end
%
% end
% end
%
% figure;
% imshow(image_enhance)
% title('Medial Axis Detection')
% opts = statset('Display','final');
% [idx,ctrs] = kmeans(X,1,...
% 'Distance','city',...
% 'Replicates',5,...
% 'Options',opts);
% %555 plot(X(idx==1,1),X(idx==1,2),'r.','MarkerSize',12)
% hold on
% plot(X(idx==2,1),X(idx==2,2),'b.','MarkerSize',12)
% plot(ctrs(:,1),ctrs(:,2),'kx',...
% 'MarkerSize',12,'LineWidth',2)
% hold off;
% ctrs(:,1)
% ctrs(:,2)
%
% figure;
% imshow(image_enhance)
% hold on;
% %rectangle('Position',[ctrs(:,1)-40, ctrs(:,2)-40, 100, 100 ],'EdgeColor','g','LineWidth',2)
% rectangle('Position',[ctrs(:,1)-40, ctrs(:,2)-40, 100, 100 ],'Curvature',[1,1],...
% 'FaceColor','k')
% title('Fitted Area');
% hold off
%
%sshold off;
% figure;
% I2 = imcrop(image_enhance,[ctrs(:,1)-40, ctrs(:,2)-40, 100, 100]);
% I2 = imresize(I2,[100 100]);
%
% imshow(I2);
% hold on;
% % rectangle('Position',[x11-30, y11+20, x12 - x11 + 10, (y12 - y11) + 10],'Curvature',[1,1],...
% % 'FaceColor','r')
% % title('Optic Disc Vessels')
% hold off;
%
%
% f= figure('visible','off'), imshow(image_enhance, 'Border', 'tight');
% rectangle('Position',[ctrs(:,1)-40, ctrs(:,2)-40, 100, 100],'Curvature',[1,1],...
% 'FaceColor','r')
% imshow(image_enhance)
% --- Executes on button press in pushbutton8.
function pushbutton8_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton8 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
pa = get(handles.inputtxt,'String');
RGB=imread(pa);
j = rgb2gray(RGB);
imshow(j)
ratio=[];
j=imresize(j,[576,768]);
var=0;for i=1:576
for k=1:768
if(j(i,k)<=1) var=var+1; end; end; end;
imshow(j)
bw=roicolor(j,150,255);
k=roicolor(j,0,255);
total=bwarea(k)-var;
total
w = sum(bw);
u = sum(bw');
[C,I] = max(w);
[c,J] = max(u);
p=bw;
blnkud=round(2*C/3);
blnklr=round(2*c/3);
for ii=(I-blnkud):(I+blnkud)
for jj=(J-blnklr):(J+blnklr)
p(jj,ii)=0;
end
end
for po=6:570
for mo=6:762
if(j(po,mo)<=1)
p(po,(mo+5))=0;
p(po,(mo-5))=0;
p((po+5),mo)=0;
p((po-5),mo)=0;
end;
end; end;
test=bwarea(p);
estimate=test/total;
count=0
HSV=rgb2hsv(RGB);H=HSV(:,:,1);
theta = -pi:0.01:pi;
for r=90:4:130
x=round(J+r*cos(theta));
y=round(I+r*sin(theta));
q=j;
chu=RGB;
for b=1:629
n=0;
while(x(1,b)<566 & y(1,b)<758 & x(1,b)>10 & y(1,b)>10 & n==0)
g(1,b)= 1000*H(x(1,b),y(1,b));
q(x(1,b),y(1,b))=255;
chu(x(1,b),y(1,b),:)=0;
if g(1,b)<105
arr(1,b)=1;
else
arr(1,b)=0;
end
n=n+1;
end
end
imshow(chu);
h=[x' y' zeros(629,1) arr'];
m=0;
for b=629:-1:1
if(arr(1,b)==1)
m=m+1;
elseif(m~=0)
h(b+1,3)=m;
m=0;
end
end
[val1,pos1]=max(h(:,3));
h(pos1,3)=0;
[val2,pos2]=max(h(:,3));
ratio1=val1/val2;
if ratio1<3
count=count+1;
ratio=[ratio ; ratio1];
end
end
problem=1.1682*sum(ratio)/count
estimate=estimate*10^6
if(problem<=1.6)
f1=0;
elseif(problem<=2)
f1=1;
else f1=2;
end;
if(estimate>5*10^3)
f2=2;
elseif(estimate>10^3)
f2=1;
else
f2=0;
end;
if(f1==2 & f2==2)
disp('Person has Severe diabetic retinopathy - Stage 8');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Person has Severe diabetic retinopathy - Stage 8\n',problem,estimate);
elseif(f1==2 & f2==1)
disp('Eye in Stage 7');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Eye in Stage 7\n',problem,estimate);
elseif(f1==1 & f2==2)
disp('Eye in Stage 6');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Eye in Stage 6\n',problem,estimate);
elseif(f1==2 & f2==0)
disp('Eye in Stage 5');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Eye in Stage 5\n',problem,estimate);
elseif(f1==1 & f2==1)
disp('Eye in Stage 4');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Eye in Stage 4\n',problem,estimate);
elseif(f1==0 & f2==2)
disp('Eye in Stage 3');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Eye in Stage 3\n',problem,estimate);
elseif(f1==1 & f2==0)
disp('Eye in Stage 2');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Eye in Stage 2\n',problem,estimate);
elseif(f1==0 & f2==1)
disp('Eye in Stage 1');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Eye in Stage 1\n',problem,estimate);
else
disp('Normal eye');
str=sprintf('\n Ratio of Vessels : %f \n\n Estimated area of cotton wool spots : %f \n\n Normal eye \n',problem,estimate);
end;
msgbox(str,'RESULT');