Point-Based Probabilistic Surfaces to Show Surface Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Modified fuzzy c-mean in medical image segmentation
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
IEEE Transactions on Visualization and Computer Graphics
Uncertainty-Aware Guided Volume Segmentation
IEEE Transactions on Visualization and Computer Graphics
An Information-theoretic Framework for Visualization
IEEE Transactions on Visualization and Computer Graphics
Positional Uncertainty of Isocontours: Condition Analysis and Probabilistic Measures
IEEE Transactions on Visualization and Computer Graphics
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
Probexplorer: uncertainty-guided exploration and editing of probabilistic medical image segmentation
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Visualization of Uncertainty without a Mean
IEEE Computer Graphics and Applications
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Probabilistic segmentation is concerned with handling imperfections of image segmentation algorithms. They assign to each voxel and each segment a probability that the voxel belongs to the segment. This is often the starting point for estimating and visualizing uncertainties in the segmentation result. We propose a novel, generally applicable uncertainty estimation approach that considers all probabilities to compute a single uncertainty value between 0 and 1 for each voxel. It is based on aspects of information theory and uses the Kullback-Leibler divergence (or the total variation divergence). We developed several forms of the proposed approach and analyze and compare their behaviors. We show the advantage over existing approaches, derive aggregated uncertainty measures that are useful for judging the accuracy of a probabilistic segmentation algorithm, and present visualization methods to highlight uncertainties in segmentation results.