2019
DOI: 10.1016/j.jpdc.2019.02.004
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Fully parallelized Lattice Boltzmann scheme for fast extraction of biomedical geometry

Abstract: We develop a fully parallel numerical method which quickly performs 2D and 3D segmentation on GPU to extract anatomical structures from medical images. The algorithm solves the level set equations completely within a Lattice Boltzmann model (LBM). Compared with existing LBM-based segmentation approaches, a parallel distance field regularization is added to the LBM computing scheme to keep computation stable with large time step iteration. This approach also avoids external regularization which has been a major… Show more

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Cited by 4 publications
(2 citation statements)
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“…1 . Since we have developed a method to do image segmentation from DICOM format and calculate the volumetric parameter and the normal direction vector of each boundary cell 29 , 49 , the STL format is not necessary. We segmented the left carotid (with a bifurcation) and vertebral (tube-like) arteries, shown in Fig.…”
Section: Application Studymentioning
confidence: 99%
See 1 more Smart Citation
“…1 . Since we have developed a method to do image segmentation from DICOM format and calculate the volumetric parameter and the normal direction vector of each boundary cell 29 , 49 , the STL format is not necessary. We segmented the left carotid (with a bifurcation) and vertebral (tube-like) arteries, shown in Fig.…”
Section: Application Studymentioning
confidence: 99%
“…Such a task is usually case to case with a lot of uncertainties, related to the imaging modality, machine resolution, scanning skills, disease conditions, etc. Although we have developed techniques for DICOM image segmentation 30 , 49 , 50 , we strongly recommend having rigorous image segmentation with expertise, instead of simply running software with extensive manual inputs, as it will affect the quantification of the hemodynamics. All the modern CAD (Computer-Aided Design) software packages such as SolidWorks export their native file format into STL format.…”
Section: Introductionmentioning
confidence: 99%