Statistically optimal interslice value interpolation in 3D medical imaging: theory and implementation

Abstract

We describe a technique for statistically optimal interslice interpolation of scalar values for use in threedimensional medical image rendering. The interpolation technique is based upon kriging. Knging is known to be the best linear unbiased estimation technique for spatially distributed data. This paper presents the results obtained using kriging in the object space pre-processing operation of slice interpolation by slice-value interpolation. As a byproduct of the technique, kriging calculates the estimation error for the interslice values. Th~s allows us to quantify the interpolation error in slices computed by the estimation technique. t

Liver tumor volume estimation

“…Interpolation methods may be broadly classified into two groups: scene-based and object-based. In scene-based techniques, the intensity values of the resulting interpolated images (scenes) are derived directly from the intensity values of the given scene [20][21][22][23][24][25][26][27]. In object-based methods [28][29][30],…”

“…In general, interpolation techniques can be divided into two categories: scenebased and object-based interpolation [31,50]. In the first case, the density values of the interpolated scene are directly determined from the density values of the given scene [20][21][22][23][24][25][26][27]. In some scene-based methods, statistical information is used to achieve the minimum estimation error [23][24][25][26][27].…”