Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Readings in uncertain reasoning
Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Level set methods for curvature flow, image enhancement, and shape recovery in medical images
Visualization and mathematics
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation Induced by Scale Invariance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Level set methods for watershed image segmentation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Accurate banded graph cut segmentation of thin structures using laplacian pyramids
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Fast, quality, segmentation of large volumes – isoperimetric distance trees
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Extracting evolving pathologies via spectral clustering
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Medical image segmentation appears to be governed by the global intensity level and should be robust to local intensity fluctuation. We develop an efficient spectral graph method which seeks the best segmentation on a stack of gamma transformed versions of the original image. Each gamma image produces two types of grouping cues operating at different ranges: Short-range attraction pulls pixels towards region centers, while long-range repulsion pushes pixels away from region boundaries. With rough pixel correspondence between gamma images, we obtain an aligned cue stack for the original image. Our experimental results demonstrate that cutting across the entire gamma stack delivers more accurate segmentations than commonly used watershed algorithms.