Level set based cerebral vasculature segmentation and diameter quantification in CT angiography
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Summary
The level set based vessel segmentation is a promising method for automated and accurate CTA diameter quantification and achieves comparable accuracy and reproducibility as the observers.
- Type
- article
- Published
- 2006-04-01
- Cited by
- 145
- References
- 47
- Access
- Open access
- OpenAlex
- https://openalex.org/W16263325
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2308398
Keywords
Candida albicans, Microbiology, Biofilm, Biology, Bacteria
References
- Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws
- Medical Image Analysis
- A review of deformable surfaces: topology, geometry and deformation
- Automatic partitioning of head CTA for enabling segmentation
- Vessel extraction in medical images by wave-propagation and traceback
- Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
- Symmetric region growing
- Statistical methods for assessing agreement between two methods of clinical measurement.
- Model-Based Detection of Tubular Structures in 3D Images
- Estimators of Tissue Proportions from X‐Ray CT Images
- CT angiography of the circle of Willis and intracranial internal carotid arteries: maximum intensity projection with matched mask bone elimination-feasibility study.
- A three-dimensional cerebrovascular flow phantom.
- Multimodality image registration by maximization of mutual information
- Geodesic Active Regions: A New Framework to Deal with Frame Partition Problems in Computer Vision
- A 3D 6-subiteration thinning algorithm for extracting medial lines
- A locally adaptive region growing algorithm for vascular segmentation
- Fast extraction of minimal paths in 3D images and applications to virtual endoscopy
- Deformable models in medical image analysis
- Level Set Methods and Fast Marching Methods
- Skeletonization for reinitialization in level-set-based vascular tree segmentation
Cited by
- Surgical Planning and Patient-Specific Biomechanical Simulation for Tracheal Endoprostheses Interventions
- Computer vision and machine learning methods for the analysis of brain and cardiac imagery
- Direct volume illustration for cardiac applications
- Filtering of thin objects : applications to vascular image analysis. (Filtrage d'objets fins : applications à l'analyse d'images vasculaires)
- An improved cerebral vessel extraction method for MRA images
- Automatic segmentation method for bone and blood vessel in murine hindlimb.
- Segmentacja trójwymiarowych obrazów ToF-SWI RM naczyń krwionosnych mózgu z wykorzystaniem filtracji wieloskalowej
- Medical Image Segmentation using Level Sets
- Vessel enhancing diffusion: A scale space representation of vessel structures
- Improving the Imaging of Small High-Density Structures in Computed Tomography
- Analyse d'images multi-modales TEP-TDM du thorax. Application à l'oncologie : segmentation de tumeurs, d'organes à risque et suivi longitudinal pour la radiothérapie
- Vessel-diameter quantification and embolus detection in CTA images
- Computational methods to predict and enhance decision-making with biomedical data.
- Using Digital Reconstructions, Morphometry, and Computational Models to Generate Novel Maps of Human Brain Vascular Architecture
- Precise Segmentation of 3-D Magnetic Resonance Angiography
- A Statistical Local Binary Fitting Model for Blood Vessel Segmentation
- Length Increasing Active Contour for the Segmentation of Small Blood Vessels
- Segmentation and Quantification of Human Vessels Using a 3-D Cylindrical Intensity Model
- Multiple hypothesis template tracking of small 3D vessel structures
- Level Set-Based Semiautomatic Bolus Segmentation in Bolus-Chasing CTA
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