A tree data structure for representing multidimensional digital binary images is described. The method is based on recursive subdivision of the d-dimensional space into 2d hyperoctants. An algorithm for constructing the tree of a d-dimensional binary image from the trees of its (d - 1 )-dimensional cross sections is given. The computational advantages of the data structure and the algorithm are demonstrated both theoretically and in application to a three-dimensional reconstruction of a human brain.
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