A Strategy for Automated Multimodality Image Registration Incorporating Anatomical Knowledge and Imager Characteristics

This paper describes two methods for automating registration of 3D medical images acquired from different modalities. One uses dispersion in an intensity based feature space as a measure of mis-registration, together with knowledge of imager characteristics. The other uses anatomical knowledge of proximity and containment between associated structures to modify a distance transform for registration. Pre-registered training images are used to customise the algorithms for specific applications. Using stochastic optimisation techniques, we automatically registered MR and CT images of the head from three patients using one training set. In each case, the accuracy of registration was comparable to that obtained by point landmark registration. We present initial results for the modified distance transform in the same clinical application, and in a new application to combine angiographic data with the surface of the brain derived from MR.

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