Towards statistically optimal interpolation for 3D medical imaging

Abstract

The use of a statistical estimation technique called kriging, which produces estimation error measurements and analyzes the volumetric grid to determine sample value variability, is described. The use of interpolation in 3D medical imaging are first reviewed. Several different interpolation techniques, including linear trilinear, and tricubic interpolation techniques, are described and assessed. The kriging statistical estimation process is presented, and the results of applying it to slice interpolation and surface visualization are reported. The results indicate the potential of kriging for interpolation in 3D medical imaging and point out the need for further work.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>