Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
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
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Registration with Uncertainties and Statistical Modeling of Shapes with Variable Metric Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orientation histograms as shape priors for left ventricle segmentation using graph cuts
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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In this paper, we propose a novel similarity-invariant approach to model-based segmentation of the left ventricle. The method assumes a control point representation of the model and an arbitrary interpolation strategy. First, we construct the prior manifold using the distributions of the relative normalized distances between pairs of control points within the training set. Then, we introduce a geometric partition of the space using a Voronoi decomposition that aims to determine relationships between the control points and the image domain. Knowledge-based segmentation can then be expressed using a Markov Random Field, where the pairwise potentials encode the variation of the shape, while the singleton potentials refer to the data term through the Voronoi decomposition of the space. State-of-the art techniques from linear programming are considered to optimize the designed function.