Surface reconstruction and representation of 3-D scenes
Pattern Recognition
A syntactic/semantic technique for surface reconstruction from cross-sectional contours
Computer Vision, Graphics, and Image Processing
Shape reconstruction from planar cross sections
Computer Vision, Graphics, and Image Processing
On active contour models and balloons
CVGIP: Image Understanding
ACM Transactions on Graphics (TOG)
Feature-based image metamorphosis
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Optimal surface reconstruction from planar contours
Communications of the ACM
Digital Image Warping
Shape Averaging and it's Applications to Industrial Design
IEEE Computer Graphics and Applications
IEEE Computer Graphics and Applications
Shape Blending Using the Star-Skeleton Representation
IEEE Computer Graphics and Applications
Conversion of complex contour line definitions into polygonal element mosaics
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
Approximating complex surfaces by triangulation of contour lines
IBM Journal of Research and Development
Morphologic Field Warping: A Volumetric Deformation Method for Medical Image Registration
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
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An interpolation method using contours of organs as the control parameters is proposed to recover the intensity information in the physical gaps of serial cross-sectional images. In our method, contour models are used for generating the control lines required for the image warping algorithm. Contour information derived from this contour-model-based segmentation process is processed and used as the control parameters to warp the corresponding regions in both input images into compatible shapes. In this way, the reliability of establishing the correspondence among different segments of the same organs is improved and the intensity information for the interpolated intermediate slices can be derived more faithfully. In comparison with the existing intensity interpolation algorithms that only search for corresponding points in a small physical neighborhood, this method provides more meaningful correspondence relationships warping regions in images into similar shapes before resampling to account for significant shape differences. Experimental results show that this method generates more close to realistic and less blurred interpolated images especially when the shape difference of corresponding contours is significant.