Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method

2021 ◽  
Vol 198 ◽  
pp. 105809
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi ◽  
Saeed Mozaffari
Keyword(s):  
Brain Tumor ◽  
Lattice Boltzmann Method ◽  
Level Set Method ◽  
Level Set ◽  
Fuzzy Clustering ◽  
Lattice Boltzmann ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Boltzmann Method

A level set method based on domain transformation and bias correction for MRI brain tumor segmentation

2021 ◽  
Vol 352 ◽  
pp. 109091
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi ◽  
Saeed Mozaffari
Keyword(s):  
Brain Tumor ◽  
Level Set Method ◽  
Level Set ◽  
Bias Correction ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Brain ◽  
Domain Transformation

scholarly journals A lattice Boltzmann method for immiscible multiphase flow simulations using the level set method

2009 ◽  
Vol 228 (4) ◽  
pp. 1139-1156 ◽  
Author(s):  
G. Thömmes ◽  
J. Becker ◽  
M. Junk ◽  
A.K. Vaikuntam ◽  
D. Kehrwald ◽  
...  
Keyword(s):  
Multiphase Flow ◽  
Lattice Boltzmann Method ◽  
Level Set Method ◽  
Level Set ◽  
Lattice Boltzmann ◽  
Flow Simulations ◽  
Boltzmann Method

Topology Optimization Based on the Level Set Method for a Thermal-Fluid Device Using the Lattice Boltzmann Method

2014 ◽  
Vol 2014.27 (0) ◽  
pp. 285-286
Author(s):  
Kentaro YAJI ◽  
Takayuki YAMADA ◽  
Masato YOSHINO ◽  
Toshiro MATSUMOTO ◽  
Kazuhiro IZUI ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Lattice Boltzmann Method ◽  
Level Set Method ◽  
Level Set ◽  
Lattice Boltzmann ◽  
Boltzmann Method

GPU-Based Level Set Method for MRI Brain Tumor Segmentation using Modified Probabilistic Clustering

2016 ◽  
pp. 106-130 ◽  
Author(s):  
Ram Kumar ◽  
Sweta Rani ◽  
Abahan Sarkar ◽  
Fazal Ahmed Talukdar
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Intrinsic Nature ◽  
Probabilistic Clustering ◽  
Mri Brain ◽  
Complex Shapes ◽  
Boltzmann Method ◽  
Robust To Noise

The level set method (LSM) has been widely used in image segmentation due to its intrinsic nature which allows handling complex shapes and topological changes easily. We propose a new level set algorithm, which is based on probabilistic c mean objective function which incorporates intensity inhomogeneity in image and robust to noise. The computational complexity of the proposed LSM is greatly reduced by using highly parallelizable lattice Boltzmann method (LBM). So the proposed algorithm is effective and highly parallelizable. The proposed LSM is implemented using Experimental results demonstrate the performance of the proposed method.

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