  In this paper, we propose a computational framework for 3D volume
reconstruction from 2D histological slices using registration algorithms in
feature space. To improve the quality of reconstructed 3D volume, first,
intensity variations in images are corrected by an intensity standardization
process which maps image intensity scale to a standard scale where similar
intensities correspond to similar tissues. Second, a subvolume approach is
proposed for 3D reconstruction by dividing standardized slices into groups.
Third, in order to improve the quality of the reconstruction process, an
automatic best reference slice selection algorithm is developed based on an
iterative assessment of image entropy and mean square error of the registration
process. Finally, we demonstrate that the choice of the reference slice has a
significant impact on registration quality and subsequent 3D reconstruction.
