Journal of Mathematical Imaging and Vision
Using partial derivatives of 3D images to extract typical surface features
Computer Vision and Image Understanding
Computing the differential characteristics of isointensity surface
Computer Vision and Image Understanding
Spatio-Temporal Image Processing: Theory and Scientific Applications
Spatio-Temporal Image Processing: Theory and Scientific Applications
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Multiresolution Analysis of Ridges and Valleys in Grey-Scale Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
In Vivo Analysis of Trabecular Bone Architecture
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
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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 驴 of the level curves and the mean curvature 驴M of the 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 in 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.