Artificial Immune Network for Automatic Point Correspondence in Medical Images

In this work, an automatic method for point-by point correspondence between medical images is presented based on the implementation of an Artificial Immune Network (AIN). AIN is a relatively novel population based algorithm, which when applied to multimodal function optimization exhibit the attractive feature of locating, the global minimum of a function, as well as a large number of strong local optimum points. In this work, AIN has been modified and applied to the problem of automatic point correspondence from pairs of images. Additionally, the proposed system is capable of altering the initially selected points on the reference image so that the population of points becomes fitter. The performance of the proposed algorithm using the AIN is evaluated against a standardized method for automatic correspondence, the template matching, in terms of the accuracy of the correspondence. Qualitative and quantitative results presented from in vitro radiographic dental images with synthetic deformations, show that the proposed algorithm outperforms the template matching for automatic point correspondence.