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Wow - that's a tricky one due to the very very low contrast. My first instinct is to suggest a different setup - if the item that you're interested in is the tan/pink thing that takes up te lower part of the image and the gray/white part at the top is just a background, then I'd change your lighting - you're using lighting that's on the camera side of the object you're looking at, but I'd go for a back light - remove all the lighting (or, at least, limit it) from the camera side, and out the item on a backlight (something like one of these from NERlite) - that will give you a high-contrast image that you can then binarize with an appropriate threshold - that will make the defect really stand out - then you can maybe determine the angle of the edge, and then try a parallel rake that's perpendicular to the edge. Shameless plug: You can find more infomation about all of these techniques, and more, in Image Acquisition and Processing with LabVIEW. <rant> You could try to solve the issue in software, but, if you can do it in hardware, you definately should! I've seen far too many setups where engineers have tried to apply countless software image transforms on top of other transforms to, essentially, get an image that the right camera/lense/lighting combination would have gotten much more consistantly. If you had a thermocouple on the end of 200 meters of noisy cable, you wouldn't try to filter out the noise in software - you'd fix the hardware setup. Image acquisition and processing is the same. As my old DAQ professor used to say: garbage in, garbage out </rant>