In binary tomography the goal is to reconstruct the innerstructure of homogeneous objects from their projections. This is usually required from a low number of projections, which are also likely to be aﬀected by noise and measurement errors. In general, the distorted and incomplete projection data holds insuﬃcient information for the correct reconstruction of the original object. In this paper, we describe two methods for approximating the local uncertainty of the reconstructions, i.e., identifying how the information stored in the projections determine each part of the reconstructed image. These methods can measure the uncertainty of the reconstruction without any knowledge from the original object itself. Moreover, we provide a global uncertainty measure that can assess the information content of a projection set and predict the error to be expected in the reconstruction of a homogeneous object. We also give an experimental evaluation of our proposed methods, mention some of their possible applications, and describe how the uncertainty measure can be used to improve the performance of the DART reconstruction algorithm.

%B COMPUTER VISION AND IMAGE UNDERSTANDING %8 2014 %@ 1077-3142 %G eng %9 Journal article %! COMPUT VIS IMAGE UND %R 10.1016/j.cviu.2014.05.006