Fundamentals of digital image processing
Fundamentals of digital image processing
Tree Approximations to Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Isoperimetric Graph Partitioning for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Isoperimetric Partitioning: A New Algorithm for Graph Partitioning
SIAM Journal on Scientific Computing
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Fast sub-voxel re-initialization of the distance map for level set methods
Pattern Recognition Letters
Robust Segmentation by Cutting across a Stack of Gamma Transformed Images
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Automatic detection and quantification of coronary calcium on 3D CT angiography data
Computer Science - Research and Development
On the complexity of isoperimetric problems on trees
Discrete Applied Mathematics
Automatic segmentation of unknown objects, with application to baggage security
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Clustering and outlier detection using isoperimetric number of trees
Pattern Recognition
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For many medical segmentation tasks, the contrast along most of the boundary of the target object is high, allowing simple thresholding or region growing approaches to provide nearly sufficient solutions for the task. However, the regions recovered by these techniques frequently leak through bottlenecks in which the contrast is low or non-existent. We propose a new approach based on a novel speed-up of the isoperimetric algorithm [1] that can solve the problem of leaks through a bottleneck. The speed enhancement converts the isoperimetric segmentation algorithm to a fast, linear-time computation by using a tree representation as the underlying graph instead of a standard lattice structure. In this paper, we show how to create an appropriate tree substrate for the segmentation problem and how to use this structure to perform a linear-time computation of the isoperimetric algorithm. This approach is shown to overcome common problems with watershed-based techniques and to provide fast, high-quality results on large datasets.