Curve and surface fitting with splines
Curve and surface fitting with splines
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized gradient vector flow external forces for active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis
International Journal of Computer Vision
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Differential Structure in non-Linear Image Embedding Functions
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Isomap and Nonparametric Models of Image Deformation
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Planar arrangement of high-dimensional biomedical data sets by isomap coordinates
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Segmenting cardiopulmonary images using manifold learning with level sets
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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In many biomedical imaging applications, video sequences are captured with low resolution and low contrast challenging conditions in which to detect, segment, or track features. When image deformations have just a few underlying causes, such as continuously captured cardiac MRI without breath-holds or gating, the captured images lie on a low-dimensional, non-linear manifold. The manifold structure of such image sets can be extracted by automated methods for manifold learning. Furthermore, the manifold structure of these images offers new constraints for tracking and segmentation of relevant image regions. We illustrate how to incorporate these new constraints within a snake-based energy minimization approach, and demonstrate improvements in using snakes to segment a set of cardiac MRI images in challenging conditions.