ArticleBiomedical engineering online2014
3D vasculature segmentation using localized hybrid level-set method.
Article in Biomedical engineering online, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Hepatic vessels segmentation using deep learning and preprocessing enhancement.Journal of applied clinical medical physics · 2023Article
- Deep Neural Network-Based Semantic Segmentation of Microvascular Decompression Images.Sensors (Basel, Switzerland) · 2021Article
- Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters.Journal of digital imaging · 2020Review
- An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures.Computational and mathematical methods in medicine · 2016Article
- Modeling of Cerebral Oxygen Transport Based on In vivo Microscopic Imaging of Microvascular Network Structure, Blood Flow, and Oxygenation.Frontiers in computational neuroscience · 2016Review
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Authors and funding
7 authors.
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Abstract
backgroundIntensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image.
methodsThis paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images.
resultsExperiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model.
conclusionsExperimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.
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