Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Improving Performance of Distribution Tracking through Background Mismatch
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiregion Level-Set Partitioning of Synthetic Aperture Radar Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
Brain MR Image Segmentation Using Local and Global Intensity Fitting Active Contours/Surfaces
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Left Ventricle Tracking Using Overlap Priors
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Active mean fields: solving the mean field approximation in the level set framework
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Efficient kernel density estimation of shape and intensity priors for level set segmentation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
4D shape priors for a level set segmentation of the left myocardium in SPECT sequences
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Segmenting and tracking the left ventricle by learning the dynamics in cardiac images
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
IEEE Transactions on Image Processing
Active contours for tracking distributions
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model
IEEE Transactions on Image Processing
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
Hi-index | 0.00 |
This study investigates active curve image segmentation with a statistical overlap constraint , which biases the overlap between the nonparametric (kernel-based) distributions of image data within the segmentation regions---a foreground and a background---to a statistical description learned a priori . We model the overlap, measured via the Bhattacharyya coefficient, with a Gaussian prior whose parameters are estimated from a set of relevant training images. This can be viewed as a generalization of current intensity-driven constraints for difficult situations where a significant overlap exists between the distributions of the segmentation regions. We propose to minimize a functional containing the overlap constraint and classic regularization terms, compute the corresponding Euler-Lagrange curve evolution equation, and give a simple interpretation of how the statistical overlap constraint influences such evolution. A representative number of statistical, quantitative, and comparative experiments with Magnetic Resonance (MR) cardiac images and Computed Tomography (CT) liver images demonstrate the desirable properties of the statistical overlap constraint. First, it outperforms significantly the likelihood prior commonly used in level set segmentation. Second, it is easy-to-learn ; we demonstrate experimentally that the Gaussian assumption is sufficient for cardiac images. Third, it can relax the need of both complex geometric training and accurate learning of the background distribution, thereby allowing more flexibility in clinical use.