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Segmentation is often an essential intermediate step in image analysis. Avolume segmentation characterizes the underlying volume image in terms ofgeometric information--segments, faces between segments, curves in whichseveral faces meet--as well as a topology on these objects. Existing algorithmsencode this information in designated data structures, but require that thesedata structures fit entirely in Random Access Memory (RAM). Today, 3D imageswith several billion voxels are acquired, e.g. in structural neurobiology.Since these large volumes can no longer be processed with existing methods, wepresent a new algorithm which performs geometry and topology extraction with aruntime linear in the number of voxels and log-linear in the number of facesand curves. The parallelizable algorithm proceeds in a block-wise fashion andconstructs a consistent representation of the entire volume image on the harddrive, making the structure of very large volume segmentations accessible toimage analysis. The parallelized C++ source code, free command line tools andMATLAB mex files are avilable fromhttp://hci.iwr.uni-heidelberg.de/software.php

How to Extract the Geometry and Topology from Very Large 3D Segmentations