Automatic derivation of initial match points for paired digital images of skin

Computerized matching of skin images has been suggested as a means of screening for changes indicative of malignant melanoma. Matching or "registering" each lesion in a pair of images plays an important role in this process. Point-pattern matching algorithms based upon correlation or geometric transformations of the image pairs have been shown to be effective for this registration but require knowledge of one or more "initial match" points which are known to be the same on both images. A method is described for automatically finding these initial match points with a high degree of success. Our algorithm uses the so-called "Gabriel graph" representation of the paired images to select sets of probable matching points. Performance of the algorithm has been measured in realistic trials, using images of patients with large numbers of pigmented lesions. Results show that the single best match chosen is correct greater than 99% of the time, while over 93% of the first three consecutive matches will be chosen correctly. It is likely that these results can be improved through the use of additional nonpositional information or additional image processing.