Discriminant Snakes for 3D Reconstruction in Medical Images

Authors:
Affiliations:
Venue:
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Year:
2000

Citing 0
Cited 2

Shape Tracking Using Centroid-Based Methods

EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Dynamic open contours using particle swarm optimization with application to fluid interface extraction

ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we propose a new statistic deformable model that we call discriminant snake for 3D reconstruction in volumetric images. Our discriminant snake generalizes the classical snake attracted by edge points, it deforms due to a generalized contour representation. The snake selects and classifies image features by a parametric classifier and each snaxel deforms to minimize the dissimilarity between the learned and found image features inside the feature space. We apply our statistic snake to segment anatomical organs and the results are very encouraging.