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

Fully Automatic 3D Reconstruction of Histological Images