An efficient alternative to compute the genus of binary volume models

In this paper we present a method to compute the Euler characteristic (X) and the genus of a volume dataset. It uses an alternative decomposition model to represent binary volume datasets: the Compact Union of Disjoint Boxes (CUDB). The method is derived from the classical method used with a voxel model and the computation of x and the genus is achieved by analyzing the connectivity among boxes and using a CUDB connected-component labeling process. We have tested our method both with phantom and real datasets and we show that it is more efficient than previous methods based on the voxel model, and other alternative models.