Fast multimodality image matching

Abstract

The diagnostic potential of medical images obtained at different times or from complimentary imaging modalities may be augmented by objective, accurate matching of the different data sets. Correlation analysis offers a powerful technique for the computation of translation, rotation, and scaling differences between image data sets, especially in the case of complimentary images containing similar but not exact information. So far, this technique suffers from the drawback of high computational expense. We have reformulated this approach, yielding a fast, computationally much less expensive algorithm. Reduction of computation time is about seventy five percent.