Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional medical imaging: algorithms and computer systems
ACM Computing Surveys (CSUR)
Reinforcement of Linear Structure using Parametrized Relaxation Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Visualization of Volume Data
IEEE Computer Graphics and Applications
Multiresolution Analysis of Ridges and Valleys in Grey-Scale Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer-Aided Interactive Object Delineation Using an Intelligent Paintbrush Technique
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Advanced algorithmic approaches to medical image segmentation
A new fuzzy relaxation algorithm for image enhancement
International Journal of Knowledge-based and Intelligent Engineering Systems
Probabilistic relaxation labelling using the Fokker-Planck equation
Pattern Recognition
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
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We propose two methods for supervised image segmentation: supervised relaxation labeling and watershed-driven relaxation labeling. The methods are particularly well suited to problems in 3D medical image analysis, where the images are large, the regions are topologically complex, and the tolerance of errors is low. Each method uses predefined cues for supervision. The cues can be defined interactively or automatically, depending on the application. The cues provide statistical region information and region topological constraints. Supervised relaxation labeling exhibits strong noise resilience. Watershed-driven relaxation labeling combines the strengths of watershed analysis and supervised relaxation labeling to give a computationally efficient noise-resistant method. Extensive results for 2D and 3D images illustrate the effectiveness of the methods.