Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-modal volume visualization using object-oriented methods
VVS '94 Proceedings of the 1994 symposium on Volume visualization
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Probabilistic Hyperstack Segmentation of MR Brain Data
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
The Topological Structure of Scale-Space Images
Journal of Mathematical Imaging and Vision
Advanced algorithmic approaches to medical image segmentation
Multiscale Segmentation of Three-Dimensional MR Brain Images
International Journal of Computer Vision
Journal of Mathematical Imaging and Vision
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Qualitative Multi-scale Feature Hierarchies for Object Tracking
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Parallel Multiresolution Image Segmentation with Watershed Transformation
ParNum '99 Proceedings of the 4th International ACPC Conference Including Special Tracks on Parallel Numerics and Parallel Computing in Image Processing, Video Processing, and Multimedia: Parallel Computation
Fuzzy Markovian segmentation in application of magnetic resonance images
Computer Vision and Image Understanding
Multivariate image analysis in biomedicine
Journal of Biomedical Informatics
Using Catastrophe Theory to Derive Trees from Images
Journal of Mathematical Imaging and Vision
Computer Vision and Image Understanding
Distributed recursive learning for shape recognition through multiscale trees
Image and Vision Computing
Robust Vessel Segmentation Based on Multi-resolution Fuzzy Clustering
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Computer Vision and Image Understanding
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Segmentation of sub-cortical structures by the graph-shifts algorithm
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Computers in Biology and Medicine
A novel fuzzy segmentation approach for brain MRI
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Tensor scale: An analytic approach with efficient computation and applications
Computer Vision and Image Understanding
| Hi-index | 0.14 |
A method is presented to segment multidimensional images using a multiscale (hyperstack) approach with probabilistic linking. A hyperstack is a voxel-based multiscale data structure whose levels are constructed by convolving the original image with a Gaussian kernel of increasing width. Between voxels at adjacent scale levels, child-parent linkages are established according to a model-directed linkage scheme. In the resulting tree-like data structure, roots are formed to indicate the most plausible locations in scale space where segments in the original image are represented by a single voxel. The final segmentation is obtained by tracing back the linkages for all roots.The present paper deals with probabilistic (or multiparent) linking, i.e., a set-up in which a child voxel can be linked to more than one parent voxel. The multiparent linkage structure is translated into a list of probabilities that are indicative of which voxels are partial volume voxels and to which extent. Probability maps are generated to visualize the progress of weak linkages in scale space when going from fine to coarser scale. This is shown to be a valuable tool for the detection of voxels that are difficult to segment properly.The output of a probabilistic hyperstack can be directly related to the opacities used in volume renderers. Results are shown both for artificial and real world (medical) images. It is demonstrated that probabilistic linking gives a significantly improved segmentation as compared with conventional (single-parent) linking. The improvement is quantitatively supported by an objective evaluation method.