Browsing by Subject "parallel image segmentation"

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    Fully parallelized Lattice Boltzmann scheme for fast extraction of biomedical geometry
    (Elsevier, 2019-06) Wang, Zhiqiang; Zhao, Ye; Yu, Huidan (Whitney); Lin, Chen; Sawchuck, Alan P.; Mechanical and Energy Engineering, School of Engineering and Technology
    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 impediment to direct parallelization of level set evolution with LBM. It allows the whole computing process to be efficiently executed on GPU. Moreover, the method can be incorporated with different image features to adopt in various image segmentation tasks. Therefore, our method enables fully GPU accelerated geometric extraction from medical images, leading to high computing performance which is demanded in many practical applications. This method is used to exactly accurate 2D and 3D anatomical structures from many real world CT and MRI images. The achieved results can also directly feed required boundary information to LBM-based hemodynamics simulation.