GPU accelerated level set model solving by lattice boltzmann method with application to image segmentation
Received:November 06, 2019  Revised:January 05, 2021  download
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Author NameAffiliationE-mail
SHI Wen-Jun Aviation Maintenance School for NCO Air Force Engineering University Xinyang 464000 China shiwen_79_1@163.com 
WANG Deng-Wei School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu 611731 China
Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu 611731 China 
wdengwei@126.com 
LIU Wan-Suo Aviation Maintenance School for NCO Air Force Engineering University Xinyang 464000 China  
JIANG Da-Gang School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu 611731 China
Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu 611731 China 
 
Abstract:A novel Graphics Processing Units (GPU) accelerated level set model which organically combines the global fitting energy and the local fitting energy from different models and the weighting coefficient of the global fitting term can be adaptively adjusted, is proposed to image segmentation. The proposed model can efficiently segment images with intensity inhomogeneity regardless of where the initial contour lies in the image. In its numerical implementation, an efficient numerical scheme called Lattice Boltzmann Method (LBM) is used to break the restrictions on time step. In addition, the proposed LBM is implemented by using a NVIDIA GPU to fully utilize the characteristics of LBM method with high parallelism. The extensive and promising experimental results from synthetic and real images demonstrate the effectiveness and efficiency of the proposed method.In addition, the factors that can have a key impact on segmentation performance are also analyzed in depth.
keywords:Intensity inhomogeneity  level set method  segmentation  Lattice Boltzmann method  graphics processing unit
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