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

William E. Higgins; Brian E. Ledell

doi:10.1117/12.175062

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

William E. Higgins, Brian E. Ledell

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	284-295
Number of pages	12
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2167
DOIs	https://doi.org/10.1117/12.175062
State	Published - May 11 1994
Event	Medical Imaging 1994: Image Processing - Newport Beach, United States Duration: Feb 13 1994 → Feb 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

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10.1117/12.175062

Cite this

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title = "A nonlinear filtering approach to gray-scale interpolation of 3D medical images",

abstract = "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.",

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T1 - A nonlinear filtering approach to gray-scale interpolation of 3D medical images

AU - Higgins, William E.

AU - Ledell, Brian E.

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N2 - 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.

AB - 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.

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Y2 - 13 February 1994 through 18 February 1994

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

Abstract

All Science Journal Classification (ASJC) codes

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