How standard are your images? Could you choose the circle whose radius and centre coordinates are within expected ranges?
If you can't do this, I'd suggest making measurements on the original image, rather than on the edge map. There have been answers here recently showing how to make measurements on the pixels in a circular disk - add a comment if you need help finding one, but do a search first.
By the way, there's a highlight which badly affects the parameters of the circle round the pupil. If you can find some way of removing it (by preprocessing prior to the edge detection perhaps) that will improve your accuracy enormously.
EDIT
Rather than counting black pixels (which means defining what is "black"), why not compare the mean of the grey-levels inside the circles? Here's a function you can use to compute these means:
function m = meanDisk(img, xc, yc, r)
%meanDisk computes mean of values inside a circle
% M = meanDisk(IMG, XC, YC, R) returns the mean of IMG(Y,X) for all X and
% Y such that the Euclidean distance of (X,Y) from (XC,YC) is less than
% R. IMG must be 2-D, R must be positive, and some elements of IMG must
% lie within the circle.
% This section is for efficiency only - avoids wasting computation time on
% pixels outside the bounding square
[sy sx] = size(img);
xmin = max(1, floor(xc-r));
xmax = min(sx, ceil(xc+r));
ymin = max(1, floor(yc-r));
ymax = min(sy, ceil(yc+r));
img = img(ymin:ymax, xmin:xmax); % trim boundaries
xc = xc - xmin + 1;
yc = yc - ymin + 1;
% Make a circle mask
[x y] = meshgrid(1:size(img,2), 1:size(img,1));
mask = (x-xc).^2 + (y-yc).^2 < r.^2;
% Compute mean
m = sum(sum(double(img) .* mask)) / sum(mask(:));
end
EDIT Adding code to deal with the four example images linked to from Ivan's comment of 12 Jan 2012.
The code below works for all 4 of the example images posted (though note that cropping may affect the results as the original image isn't in quite the same place in all of them). I used the fact that the pupil is dark to detect the case where the eye is closed - this works better than playing with the Hough transform parameters.
% Read in the image as downloaded, crop out the starting image, convert to
% grayscale in range 0-1
img = imread('help1.jpg'); % or whatever
img = double(rgb2gray(img(28:169, 167:391, :)))/256;
% Find edges. Note that Canny method incorporates Gaussian smoothing and
% gradient calculation, so no need to do these separately
edgeim = edge(img, 'canny');
% Detect most prominent circle. Radius range may be overgenerous.
radii = 7:2:21;
[h, margin] = circle_hough(edgeim, radii);
peak = circle_houghpeaks(h, radii, margin, 'npeaks', 1);
cx = peak(1);
cy = peak(2);
r = peak(3);
% Test whether the brightness inside the circle is greater than the average
% image brightness divided by the following constant. If it is, the pupil
% is probably not visible.
pupil_darkness_ratio = 2;
if pupil_darkness_ratio*meanDisk(img, cx, cy, r) > mean(img(:))
disp('No pupil found');
else
imshow(img);
hold on;
[x, y] = circlepoints(r);
plot(x+cx, y+cy, 'g-');
hold off
end
