A matlab package for ND bezier curves operations

A bezier curve is parametrized by controlPts - which is [N x dim] for N control points of dimension dim. Note that we use matlab matrix ordering, so the first dimension will be treated as 'y' in the 2D case.

Current function support:

• bezier.eval evaluate the curve at many points
• bezier.draw draw the curve in a image or volume
• bezier.view visualize 2D or 3D bezier curves (even multiple curves in the same figure)
• bezier.explore explore 2D bezier curves interactively

If you find this code useful, please cite the following paper, for which the code was developed:

Adrian V. Dalca, Giovanna Danagoulian, Ron Kikinis, Ehud Schmidt, Polina Golland
Segmentation of nerve bundles and ganglia in spine MRI using particle filters
MICCAI 2011

@inproceedings{dalca2011segmentation,
  title={Segmentation of nerve bundles and ganglia in spine MRI using particle filters},
  author={Dalca, Adrian and Danagoulian, Giovanna and Kikinis, Ron and Schmidt, Ehud and Golland, Polina},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={537--545},
  year={2011},
  organization={Springer}
}

Each method has several helpful options, run help +bezier for more help.
Run beziertest for thorough examples and to see the extent of possibilities.

For quick use try the following:

• plot of a 2D bezier curve:

t = bezier.eval([1, 2; 5, 5; 7, 4]);
plot(t(:, 1), t(:, 2));

• draw a 2D bezier curve in an image:

vol = bezier.draw([1, 2; 50, 50; 70, 40]);
imagesc(vol); colormap gray;

• visualize a 2D curve directly:

bezier.view([1, 2; 70, 70; 70, 130]);

• visualize a 3D curve directly:

bezier.view([1, 2, 7; 5, 5, 9; 9, 9, 9]);

• explore the curves:

bezier.explore;

Adrian Dalca