Publication 2008 Issue No. 12 - December Abstract - Euclidean Skeletons of Digital Image and Volume Data in Linear Time by the Integer Medial Axis Transform
Euclidean Skeletons of Digital Image and Volume Data in Linear Time by the Integer Medial Axis Transform
December 2008 (vol. 30 no. 12)
pp. 2204-2217
 ASCII Text x Wim H. Hesselink, Jos B.T.M. Roerdink, "Euclidean Skeletons of Digital Image and Volume Data in Linear Time by the Integer Medial Axis Transform," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 12, pp. 2204-2217, December, 2008.
 BibTex x @article{ 10.1109/TPAMI.2008.21,author = {Wim H. Hesselink and Jos B.T.M. Roerdink},title = {Euclidean Skeletons of Digital Image and Volume Data in Linear Time by the Integer Medial Axis Transform},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {30},number = {12},issn = {0162-8828},year = {2008},pages = {2204-2217},doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.21},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - Euclidean Skeletons of Digital Image and Volume Data in Linear Time by the Integer Medial Axis TransformIS - 12SN - 0162-8828SP2204EP2217EPD - 2204-2217A1 - Wim H. Hesselink, A1 - Jos B.T.M. Roerdink, PY - 2008KW - CurveKW - surfaceKW - solidKW - and object representationsKW - Image RepresentationKW - Edge and feature detectionKW - Feature representationKW - ShapeKW - Volume visualizationVL - 30JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -
Wim H. Hesselink, University of Groningen, Groningen
Jos B.T.M. Roerdink, University of Groningen, Groningen
A general algorithm for computing Euclidean skeletons of 2D and 3D data sets in linear time is presented. These skeletons are defined in terms of a new concept, called the integer medial axis (IMA) transform. We prove a number of fundamental properties of the IMA skeleton, and compare these with properties of the CMD (centers of maximal disks) skeleton. Several pruning methods for IMA skeletons are introduced (constant, linear and square-root pruning) and their properties studied. The algorithm for computing the IMA skeleton is based upon the feature transform, using a modification of a linear-time algorithm for Euclidean distance transforms. The skeletonization algorithm has a time complexity which is linear in the number of input points, and can be easily parallelized. We present experimental results for several data sets, looking at skeleton quality, memory usage and computation time, both for 2D images and 3D volumes.

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Index Terms:
Curve, surface, solid, and object representations, Image Representation, Edge and feature detection, Feature representation, Shape, Volume visualization
Citation:
Wim H. Hesselink, Jos B.T.M. Roerdink, "Euclidean Skeletons of Digital Image and Volume Data in Linear Time by the Integer Medial Axis Transform," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 12, pp. 2204-2217, Dec. 2008, doi:10.1109/TPAMI.2008.21