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Ridge-like structures in digital images may be extracted by convolving the images with derivatives of Gaussians. The choice of the convolution operator and of the parameters involved defines a specific ridge image. In this paper, various ridge measures related to isophote curvature are constructed, reviewed, and evaluated with respect to their usability in CT/MRI matching of human brain scans. Construction is initially done using heuristics in two-dimensional images, and then established firmly in a mathematical framework. Attention is paid to the necessity of operator invariance, scale of the operator, extension to three-dimensional images, and relations to isophote and principal curvature. It will be shown that one of the ridge measures appears well suited for the purpose of matching, despite the fact that the measure fails to detect ridges in a number of stylized scenes.

Describes an automated approach to register CT and MR brain images. Differential operators in scale space are applied to each type of image data, so as to produce feature images depicting "ridgeness". The resulting CT and MR feature images show similarities which can be used for matching. No segmentation is needed and the method is devoid of human interaction. The matching is accomplished by hierarchical correlation techniques. Results of 2-D and 3-D matching experiments are presented. The correlation function ensures an accurate match even if the scanned volumes to be matched do not completely overlap, or if some of the features in the images are not similar.

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