A nonlinear filtering approach to gray-scale interpolation of 3D medical images

William E. Higgins, Brian E. Ledell

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Three-dimensional (3D) 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-filterbased 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.

Original languageEnglish (US)
Pages (from-to)284-295
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - May 11 1994
EventMedical Imaging 1994: Image Processing - Newport Beach, United States
Duration: Feb 13 1994Feb 18 1994

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'A nonlinear filtering approach to gray-scale interpolation of 3D medical images'. Together they form a unique fingerprint.

Cite this