Feature-based nonrigid image registration using multi-class Hausdorff fractions

In a previous paper (Ref. 9) we presented a feature-based nonrigid image registration method using a Hausdorff distance based matching measure. One limitation of the method is that it is likely to fail in "ambiguous" cases where a part of the features in the source image are nearer to a prominent number of non-corresponding features in the target image than to their corresponding ones. To partly alleviate this limitation, in this paper we propose a new feature-based nonrigid image method that uses multi-class-Hausdorff-fractions-based similarity matching measure. We first divide features into a finite number of classes, then we calculate a similarity matching measure by adding up the forward and backward multi-class Hausdorff fractions of the classes. The new similarity matching measure outperforms that used in our previous work, given that the features in the images to be registered can be correctly classified. We also adapted the optimization procedure of our previous method so that it can work appropriately with the new similarity matching measure. The new method, introducing only a small computational load, is capable of reducing undesired matching of features that are adjacent to each other but belong to different classes.