<title>Nonlinear filtering approach to grayscale interpolation of 3D medical images</title>

Abstract

Three-dimensional images are now common in radiology. A 3D image is formed by stacking a contiguous sequence of two-dimensional cross-sectional images, or slices. Typically, the spacing between known slices is greater than the spacing between known points on a slice. Many visualization and image-analysis tasks, however, require the 3D image to have equal sample spacing in all directions. To meet this requirement, one applies an interpolation technique to the known 3D image to generate a new uniformly sampled 3D image. We propose a nonlinear-filter-based approach to gray-scale interpolation of 3D images. The method, referred to as column-fitting interpolation, is reminiscent of the maximum-homogeneity filter used for image enhancement. The method is typically more effective than traditional gray-scale interpolation techniques.