Creases are a type of ridge/valley structures that can be characterized by local conditions: Therefore, creaseness refers to local ridgeness and valleyness. The curvature kappa of Me level curves and the mean curvature kappa(M) of Me level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness an many applications. We propose a new creaseness measure, based on these curvatures: that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy.
|Number of pages||6|
|Journal||Proceedings - Ieee Computer Society Conference On Computer Vision And Pattern Recognition|
|Publication status||Published - 1998|