Unable to Segment Iris from Preprocessed Eye Dataset.

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The following are some images from my dataset:
I convert the coloured images in grayscale and preprocess them as follows:
function Eye= preprocessing(eyeimage)
if(size(eyeimage, 3) == 3) % if RGB image is inputted
eyeimage = rgb2gray(eyeimage);
end
%%%%%%%%% SHARPENING THE IMAGE %%%%%%%%%%%%%%%%
dimage = im2double(eyeimage);
gradient = convn(dimage,ones(3)/9,"same") - convn(dimage,ones(5)/25,"same");
amount = 5;
eyeimage = dimage + amount.*gradient;
% figure(),imshowpair(dimage,eyeimage,'montage');
% title("Original image (left) vs sharpened image (right)");
%%%%%%%%%%%%%%% Contrast-limited adaptive histogram equalization (CLAHE)%%%
% Eye = adapthisteq(eyeimage,'clipLimit',0.02,'Distribution','rayleigh');
Eye=histeq(eyeimage);
The result is:
After segemting iris from this preprocessed image:
I am not getting the correct segmented iris:
%%%%%%%%%%%%%%%%%%%%%%%% CREATEIRISTEMPLATE %%%%%%%%%%%%%%%%%%
%normalisation parameters
radial_res = 20;
angular_res = 240;
% with these settings a 9600 bit iris template is created
%feature encoding parameters
nscales=1;
minWaveLength=18;
mult=1; % not applicable if using nscales = 1
sigmaOnf=0.5;
%%%%%%%%%%%%%%%%%%%%%%%% SEGMENTIRIS %%%%%%%%%%%%%%%%%%
%CASIA
lpupilradius = 28;
upupilradius = 75;
lirisradius = 80;
uirisradius = 150;
% define scaling factor to speed up Hough transform
scaling = 0.3;
reflecthres = 240;
% find the iris boundary
[row, col, r] = findcircle(eyeimage, lirisradius, uirisradius, scaling, 2, 0.45, 0.19, 1.00, 0.00);
circleiris = [row col r];
rowd = double(row);
cold = double(col);
rd = double(r);
irl = round(rowd-rd);
iru = round(rowd+rd);
icl = round(cold-rd);
icu = round(cold+rd);
imgsize = size(eyeimage);
if irl < 1
irl = 1;
end
if icl < 1
icl = 1;
end
if iru > imgsize(1)
iru = imgsize(1);
end
if icu > imgsize(2)
icu = imgsize(2);
end
% to find the inner pupil, use just the region within the previously
% detected iris boundary
imagepupil = eyeimage( irl:iru,icl:icu);
%find pupil boundary
[rowp, colp, r] = findcircle(imagepupil, lpupilradius, upupilradius,0.2,2,0.35,0.35,1.00,1.00);
rowp = double(rowp);
colp = double(colp);
r = double(r);
row = double(irl) + rowp;
col = double(icl) + colp;
row = round(row);
col = round(col);
circlepupil = [row col r];
% set up array for recording noise regions
% noise pixels will have NaN values
imagewithnoise = double(eyeimage);
%find top eyelid
topeyelid = imagepupil(1:(rowp-r),:);
lines = findline(topeyelid);
if size(lines,1) > 0
[xl yl] = linecoords(lines, size(topeyelid));
yl = double(yl) + irl-1;
xl = double(xl) + icl-1;
yla = max(yl);
y2 = 1:yla;
ind3 = sub2ind(size(eyeimage),yl,xl);
imagewithnoise(ind3) = NaN;
imagewithnoise(y2, xl) = NaN;
end
%find bottom eyelid
bottomeyelid = imagepupil((rowp+r):size(imagepupil,1),:);
lines = findline(bottomeyelid);
if size(lines,1) > 0
[xl yl] = linecoords(lines, size(bottomeyelid));
yl = double(yl)+ irl+rowp+r-2;
xl = double(xl) + icl-1;
yla = min(yl);
y2 = yla:size(eyeimage,1);
ind4 = sub2ind(size(eyeimage),yl,xl);
imagewithnoise(y2, xl) = NaN;
end
imagewithnoise(ind4) = NaN;
%For CASIA, eliminate eyelashes by thresholding
ref = eyeimage < 100;
coords = find(ref==1);
imagewithnoise(coords) = NaN;
% figure,imshow(eyeimage);title('originalimage');
% figure,imshow(imagepupil);title('pupil');
% figure,imshow(imagewithnoise);title('image with noise');
% WRITE NOISE IMAGE
%
imagewithnoise2 = uint8(imagewithnoise);
imagewithcircles = uint8(eyeimage);
%get pixel coords for circle around iris
[xii,yii] = circlecoords([circleiris(2),circleiris(1)],circleiris(3),size(eyeimage));
ind2 = sub2ind(size(eyeimage),double(yii),double(xii));
%get pixel coords for circle around pupil
[xpp,ypp] = circlecoords([circlepupil(2),circlepupil(1)],circlepupil(3),size(eyeimage));
ind1 = sub2ind(size(eyeimage),double(ypp),double(xpp));
% Write noise regions
imagewithnoise2(ind2) = 255;
imagewithnoise2(ind1) = 255;
% Write circles overlayed
imagewithcircles(ind2) = 255;
imagewithcircles(ind1) = 255;
% figure('name',"Hough Transform locating Iris and Pupil");
% subplot(1,3,3);imshow(eyeimage);title('Original image');
% subplot(1,3,1);imshow(imagewithnoise2);title('Segmented');
% subplot(1,3,2);imshow(imagewithcircles);title('Circles');
%%%%%%%%%%%%%%%%%% MODEL2OUTPUT %%%%%%%%%%%%%%%%%
x=xii;
y=yii;
r1=ind2;
xp=xpp;
yp=ypp;
r2=ind1;
[nx,ny,d] = size(I) ;
[X,Y] = meshgrid(1:ny,1:nx) ;
th = linspace(0,2.*pi);
x=double(x);
y=double(y);
r1=double(r1);
xp=double(xp);
yp=double(yp);
r2=double(r2);
idx = inpolygon(X(:),Y(:),x,y) ;
for i = 1:d
I1 = I(:,:,i) ;
I1(~idx) = 255 ;
I(:,:,i) = I1 ;
end
idx = inpolygon(X(:),Y(:),xp,yp) ;
for i = 1:d
I1 = I(:,:,i) ;
I1(idx) = 255 ;
I(:,:,i) = I1 ;
end
figure('name',"Segmented Iris"),imshow(I);
I think the problem is in the findcircle parameters. Can anyone help me with the parameters I must put in findcircle function?
Attaching the findcircle file for the reference.

采纳的回答

Taylor
Taylor 2024-2-13
Have you considered just using imfindcircles?
  9 个评论
Taylor
Taylor 2024-2-14
There are various ways to fine tune imfindcircles. Namely, using a radius range and adjusting the values for sensitivity and edge threshold.

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