Advanced Methods in Variational Learning: Segmentation with Intensity Inhomogeneity

2015 ◽  
pp. 171-187
Author(s):  
Jack Spencer ◽  
Ke Chen
Keyword(s):  
Intensity Inhomogeneity ◽  
Variational Learning

scholarly journals Variational learning from implicit bandit feedback

2021 ◽  
Author(s):  
Quoc-Tuan Truong ◽  
Hady W. Lauw
Keyword(s):  
Variational Learning

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

An Improved Image Segmentation Active Contour Model

2014 ◽  
Vol 513-517 ◽  
pp. 3463-3467
Author(s):  
Li Fen Zhou ◽  
Chang Xu Cai
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Local Region ◽  
Intensity Inhomogeneity ◽  
Contour Model ◽  
Global And Local ◽  
Region Information

The Chan-Vese (C-V) active contour model has low computational complexity, initialization and insensitive to noise advantagesand utilizes global region information of images, so it is difficult to handle images with intensity inhomogeneity. The Local binary fitting (LBF) model based on local region information has its certain advantages in mages segmentation of weak boundary or uneven greay.but , the segmentation results are very sensitive to the initial contours, In order to address this problem, this paper proposes a new active contour model with a partial differential equation, which integrates both global and local region information. Experimental results show that it has a distinctive advantage over C-V model for images with intensity inhomogeneity, and it is more efficient than LBF.

Liver MRI segmentation with edge-preserved intensity inhomogeneity correction

2017 ◽  
Vol 12 (4) ◽  
pp. 791-798 ◽  
Author(s):  
Hui Liu ◽  
Pinpin Tang ◽  
Dongmei Guo ◽  
HaiXia Liu ◽  
Yuanjie Zheng ◽  
...  
Keyword(s):  
Intensity Inhomogeneity ◽  
Liver Mri ◽  
Mri Segmentation ◽  
Inhomogeneity Correction

Variational Learning of the Mixture of Shifted-Scaled Dirichlet Distributions via Entropy Splitting

2021 ◽  
Author(s):  
Ahmed Rebei ◽  
Oumayma Dalhoumi ◽  
Narges Manouchehri ◽  
Ali Baghdadi ◽  
Manar Amayri ◽  
...  
Keyword(s):  
Dirichlet Distributions ◽  
Variational Learning

scholarly journals Local and Global Active Contour Model for Image Segmentation With Intensity Inhomogeneity

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 54224-54240 ◽  
Author(s):  
Qing Cai ◽  
Huiying Liu ◽  
Yiming Qian ◽  
Jing Li ◽  
Xiaojun Duan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Contour Model
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