A new similarity measure for multi-modal image registration

Multi-modal similarity measures are required to register images of the same object using different sensors. This registration is often required for medical images of the same patient captured using different imaging modalities such as MRI, CT and PET. In this paper, a new multi-modal similarity measure is proposed which is based on calculating the sum-of-conditional variances from the joint histogram of the two images to be registered. The formulation of this new similarity measure allows the standard Gauss-Newton optimization procedure to be used. Our experimental results show that this new approach is more accurate and robust than the most common and best performing alternative and is also more computationally efficient.