Cognition-based MRI brain tumor segmentation technique using modified level set method

2018 ◽  
Vol 21 (3) ◽  
pp. 357-369 ◽  
Author(s):  
Virupakshappa ◽  
Basavaraj Amarapur
Keyword(s):  
Brain Tumor ◽  
Level Set Method ◽  
Level Set ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Brain ◽  
Segmentation Technique

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

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.

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

2017 ◽  
pp. 1053-1078
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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