This is very easy, or can be. Just threshold. If you have noise then call imfill() and bwareafilt() to fill and extract the largest blob. Otherwise, simply use find(binaryImage, 1, 'first') to get the x location of the edge. Do for every row and then put into polyfit(x,y,1) to get the equation of a line. Then get the fitted coordinates of a perfect line. then compute the residuals (distances of actual edge to fitted edge). If the residual for any line is more than some certain amount, then there is a defect at that line.
Nowhere in what I said is an edge detection filter required.
Basically something like this:
binaryImage = grayImage > someValue;
binaryImage = imfill(binaryImage, 'holes'); % Optional. Delete for speed
binaryImage = bwareafilt(binaryImage, 1); % Optional, if there is noise in the dark left half.
[rows, columns] = size(binaryImage);
for y = 1 : rows
x(y) = find(binaryImage(y, :), 1, 'first');
end
% Switch x and y so y (line/row) is the independent variable.
y = x;
x = 1 : rows
coefficients = polyfit(x, y, 1); % Fit a line
yFitted = polyval(coefficients, x);
% Scan every line looking for deviation from fitted line
residuals = abs(y - yFitted);
% See if there are any big differences - find out what rows they're on.
defectLines = differences > someThreshold;
and so on. That's just off the otp of my head - not tested - so there may be errors but at least it will give you a start.
