  Magnetic Resonance Imaging (MRI) is a technology for non-invasive imaging of
anatomical features in detail. It can help in functional analysis of organs of
a specimen but it is very costly. In this work, methods for (i) virtual
three-dimensional (3D) reconstruction from a single sequence of two-dimensional
(2D) slices of MR images of a human spine and brain along a single axis, and
(ii) generation of missing inter-slice data are proposed. Our approach helps in
preserving the edges, shape, size, as well as the internal tissue structures of
the object being captured. The sequence of original 2D slices along a single
axis is divided into smaller equal sub-parts which are then reconstructed using
edge preserved kriging interpolation to predict the missing slice information.
In order to speed up the process of interpolation, we have used multiprocessing
by carrying out the initial interpolation on parallel cores. From the 3D matrix
thus formed, shearlet transform is applied to estimate the edges considering
the 2D blocks along the $Z$ axis, and to minimize the blurring effect using a
proposed mean-median logic. Finally, for visualization, the sub-matrices are
merged into a final 3D matrix. Next, the newly formed 3D matrix is split up
into voxels and marching cubes method is applied to get the approximate 3D
image for viewing. To the best of our knowledge it is a first of its kind
approach based on kriging interpolation and multiprocessing for 3D
reconstruction from 2D slices, and approximately 98.89\% accuracy is achieved
with respect to similarity metrics for image comparison. The time required for
reconstruction has also been reduced by approximately 70\% with multiprocessing
even for a large input data set compared to that with single core processing.
