The efficacy of using intensity edges, curvature of iso-intensity contours, and tissue classified data for image matching are examined. The image matching problem is formulated in such a way that the different features are handled uniformly, allowing the same code to be used in each instance. The results using both simulated and real brain images indicate that each feature affected and improvement in the correspondence after matching with it.
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