Geometry driven multimodality matching of brain images

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Abstract
Clinical diagnosis, as well as therapy planning and evaluation, are increasingly supported by multimodal images. There are many instances desiring integration of the information obtained by various imaging devices. This paper describes a new approach to match images of different modalities. Differential operators are used in combination with Gaussian blurring to extract geometric features from the images that correspond to similar structures. The resulting ‘feature’ images may be used with existing matching techniques that minimize the distance between the features in the images to be matched. Our first application of this new approach concerns matching of MRI and CT brain images. The so-called Lυυ operator produces a ridge-like feature image from which in CT and MRI the center curve of the cranium is easily extracted. First results of this operator's performance in matching tasks are shown. Another promising operator is the ‘umbilicity’ operator, which is presented in combination with SPECT images.