ArticleBioMed research international2014
A vessel active contour model for vascular segmentation.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artifact-robust Deep Learning-based Segmentation of 3D Phase-contrast MR Angiography: A Novel Data Augmentation Approach.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Quantitative analysis validation for sclerotherapy treatment of lower limb telangiectasias.Journal of vascular surgery. Venous and lymphatic disorders · 2023Article
- Hepatic vessels segmentation using deep learning and preprocessing enhancement.Journal of applied clinical medical physics · 2023Article
- Two new stenosis detection methods of coronary angiograms.International journal of computer assisted radiology and surgery · 2022Article
- Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.European radiology · 2022Article
- A Three-Dimensional Deep Convolutional Neural Network for Automatic Segmentation and Diameter Measurement of Type B Aortic Dissection.Korean journal of radiology · 2021Article
- Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network.Nature communications · 2020Article
- 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
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Authors and funding
8 authors.
Funding
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Abstract
This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.
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