## Reconstruction of 3D medical images: A nonlinear interpolation technique for reconstruction of 3D medical images (1991)

Venue: | Computer Vision, Graphics, and Image Processing 53(4):382--391 |

Citations: | 5 - 0 self |

### BibTeX

@INPROCEEDINGS{Chang91reconstructionof,

author = {Long-wen Chang and Hown-wen Chen and Ju-rone Ho},

title = {Reconstruction of 3D medical images: A nonlinear interpolation technique for reconstruction of 3D medical images},

booktitle = {Computer Vision, Graphics, and Image Processing 53(4):382--391},

year = {1991}

}

### Years of Citing Articles

### OpenURL

### Abstract

Three-dimensional medical images reconstructed from a series of two-dimensional images produced by computerized tomography, magnetic resonance imaging, etc., present a valuable tool for modem medicine. Usually, the inter-resolution between two cross sections is less than the intraresolution within each cross section. Therefore, interpolations are required to create a 3D visualization. Many techniques, including voxel-based and patch tiling methods, apply linear interpolations between two cross sections. Although those techniques using linear interpolations are economical in computation, they need much cross-sectional data and are unable to enlarge because of aliasmg. Hence, the techniques that apply two-dimensional nonlinear interpolation functions among cross sections were proposed. In this paper, we introduce the curvature sampling of the contour of a medical object in a CT (computerized tomography) image. Those sampled contour points are the candidates for the control points of Hermite surfaces between each pair of cross sections. Then, a nearest-neighbor mapping of control points between every two cross sections is used for surface formation. The time complexity of our mapping algorithm is O(m + n), where m and II are the numbers of control points of two cross sections. It is much faster than Kehtamavaz and De Figueiredo’s merge method, whose time complexity is O(n’m~). 0 1991 Academic Press, Inc. 1.

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