Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Geometric Approach to Segmentation and Analysis of 3D Medical Images
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
International Journal of Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis
International Journal of Computer Vision
Level Sets and Distance Functions
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Reconciling Distance Functions and Level Sets
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Automatic cardiac MRI segmentation using a biventricular deformable medial model
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
3D Graph cut with new edge weights for cerebral white matter segmentation
Pattern Recognition Letters
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The problem of segmenting a volumetric layer of finite thickness is encountered in several important areas within medical image analysis. Key examples include the extraction of the cortical gray matter of the brain and the left ventricle myocardium of the heart. The coupling between the two bounding surfaces of such a layer provides important information that helps to solve the segmentation problem. Here we propose a new approach of coupled surfaces propagation via level set methods, which takes into account coupling as an important constraint. By evolving two embedded surfaces simultaneously, each driven by its own image-derived information while maintaining the coupling, we capture a representation of the two bounding surfaces and achieve automatic segmentation on the layer. Characteristic gray level values, instead of image gradient information alone, are incorporated in deriving the useful image information to drive the surface propagation, which enables our approach to capture the homogeneity inside the layer. The level set implementation offers the advantage of easy initialization, computational efficiency and the ability to capture deep folds of the sulci. As a test example, we apply our approach to unedited 3D Magnetic Resonance (MR) brain images. Our algorithm automatically isolates the brain from non-brain structures and recovers the cortical gray matter.