The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
A New Algorithm for Energy Minimization with Discontinuities
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilevel Banded Graph Cuts Method for Fast Image Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Space-Time Multi-Resolution Banded Graph-Cut for Fast Segmentation
Proceedings of the 30th DAGM symposium on Pattern Recognition
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
Uncertainty driven multi-scale optimization
Proceedings of the 32nd DAGM conference on Pattern recognition
SlimCuts: graphcuts for high resolution images using graph reduction
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Vessels-Cut: a graph based approach to patient-specific carotid arteries modeling
3DPH'09 Proceedings of the 2009 international conference on Modelling the Physiological Human
Smooth Chan-Vese segmentation via graph cuts
Pattern Recognition Letters
Simultaneous segmentation and filtering via reduced graph cuts
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Efficient pixel-grouping based on dempster's theory of evidence for image segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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The Graph Cuts method of interactive segmentation has become very popular in recent years. This method performs at interactive speeds for smaller images/volumes, but an unacceptable amount of storage and computation time is required for the large images/volumes common in medical applications. The Banded Graph Cut (BGC) algorithm was proposed to drastically increase the computational speed of Graph Cuts, but is limited to the segmentation of large, roundish objects. In this paper, we propose a modification of BGC that uses the information from a Laplacian pyramid to include thin structures into the band. Therefore, we retain the computational efficiency of BGC while providing quality segmentations on thin structures. We make quantitative and qualitative comparisons with BGC on images containing thin objects. Additionally, we show that the new parameter introduced in our modification provides a smooth transition from BGC to traditional Graph Guts.