Melanoma is a kind of skin cancer so common and obvious at the same time that doctors encourage self-detection, even on TV, which is unusual. The detection is based on the mnemonic ABCDE:

• Asymmetry of the spot.
• Border: irregular.
• Color: varies.
• Diameter: large.
• Evolution of the spot.

There are many images out there that illustrate these features but the one above is one of very few I could find that compares benign lesions to cancerous ones. E is missing which is not uncommon.

The tests seem so simple, it’s tempting to try to design an image analysis system that would detect this cancer. Even though it’s been probably done before, let’s see how far we can go within the limits of a single blog post.

There is clearly an overlap between A, B and D. I will try to separate them as much as possible.

A for Asymmetry: How do we detect that by image analysis? On the face of it, this is about the measure of symmetry of the spot. If this is the case, the simplest approach is the following. Find the major axes of the spot, then carry out reflections about these axes and compute the overlap of the resulting spots (3 total) with the original. The overlap should be computed in relative terms in order to separate A from D.

Of course to detect even more symmetry one need to look at all rotations but that may be unnecessary.

Another, even simpler, approach is to compute the roundness of the spot as “[m]ost moles - the kind you usually don’t have to worry about - are more or less round.” That may have to be preceded by smoothing the border (Gaussian blur or similar) in order to separate A from B.

Either approaches has its problems: the major axis are badly affected by noise while computing the roundness produces errors even without noise.

To be continued…