Surface Skeletons Detected on the D6 Distance Transform
We present an algorithm for extracting the surface skeleton of a 3D object from its D 6 distance transform. The skeletal voxels are directly detected and marked on the distance transform within a small number of inspections, independent of object thickness. This makes the algorithm preferable with respect to algorithms based on iterative application of topology preserving removal operations, when working with thick objects. The set of skeletal voxels is centred within the object, symmetric, and topologically correct. It is at most 2-voxel wide (except for some cases of surface intersections) and includes all centres of maximal D6 balls, which makes skeletonization reversible. Reduction to a unit wide surface skeleton can be obtained by suitable post-processing.
- C. Arcelli and G. Sanniti di Baja. A one-pass two-operation process to detect the skeletal pixels on the 4-distance transform. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(4):411–414, Apr. 1989.Google Scholar
- G. Bertrand and G. Malandain. A new characterization of three-dimensional simple points. Pattern Recognition Letters, 15:169–175, Feb. 1994.Google Scholar
- G. Borgefors, I. Nyström, and G. Sanniti di Baja. Surface skeletonization of volume objects. In P. Perner, P. Wang, and azriel Rosenfeld, editors, Advances in Structural and Syntactical Pattern Recognition (SSPR’96), pages 251–259, Leipzig, Germany, Aug. 1996. Springer-Verlag. LNCS 1121.Google Scholar
- G. Borgefors, I. Nyström, and G. Sanniti di Baja. Connected components in 3D neighbourhoods. In M. Frydrych, J. Parkkinen, and A. Visa, editors, Proceedings of 10th Scandinavian Conference on Image Analysis (SCIA’97), pages 567–572, Lappeenranta, Finland, 1997. Pattern Recognition Society of Finland.Google Scholar
- G. Borgefors, I. Nyström, and G. Sanniti di Baja. Computing skeletons in three dimensions. Pattern Recognition, 32(7):1225–1236, July 1999.Google Scholar
- P. K. Saha and B. B. Chaudhuri. 3D digital topology under binary transformation with applications. Computer Vision and Image Understanding, 63(3):418–429, May 1996.Google Scholar