Brain tumor segmentation in 3D MRIs using an improved Markov random field model

Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

doi:10.1117/12.913743

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Typical methods of supervised strategy include atlas-based methods, k-nearest neighbor, support vector machines, artificial neural networks, Markov random field (MRF) [9], random forests, Bayesian inference and machine learningbased methods. Oden et.al [11] address the mathematical and computational models of using methods of Bayesian inference. Teferra et al [12] propose a Bayesian model calibration framework to evaluate brain tissue characterization.…”

Section: Introductionmentioning

confidence: 99%

An adaptive sparse Bayesian model combined with joint information-based label fusion for brain tumor segmentation in MRI

Wang

Luan

et al. 2021

SIViP

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This paper focuses on the automatic segmentation of brain tumor in MR image. The first focus is an algorithm that applies the sparse Bayesian decision theorem combined with the Markov random field and Gibbs random field for the segmentation of brain tumor in T1-w, T1-c, T2-w and FLAIR image, respectively. The second focus is the joint label fusion algorithm based on intensity information and spatial information. We employ the spatial information of inner voxels in reliable regions to help the segmentation of boundary voxels in non-reliable regions in label fusion process. The proposed approach is evaluated by 20 training cases obtained from Medical Image Computing and Computer Aided Intervention Society (MICCAI) 2017 Brain Tumor Segmentation Challenge. A comparison of the proposed approach and other conventional approaches is presented in terms of Dice, Sensitivity and Hausdorff_95. Furthermore, the total tumor volume is calculated and compared with expert delineation. It can be seen that the proposed approach is able to acquire a larger mean value of Dice and Sensitivity than the other conventional approaches. This novel approach has an added value for the clinical evaluation of tumor patients. Keywords Brain tumor segmentation in MRI • Multiple-atlas-based label fusion • Sparse Bayesian decision theorem

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Section: Introductionmentioning

confidence: 99%

An adaptive sparse Bayesian model combined with joint information-based label fusion for brain tumor segmentation in MRI

Wang

Luan

et al. 2021

SIViP

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“…Yousefi et al have proposed a hybrid of SA and improved genetic algorithm (IGA) in order to decrease the computational burden of MRF. They have shown that by combining SA and IGA, an efficient exploration of search space can achieve plus the proposed approach uses benefits of SA and IGA and alleviates their shortcomings simultaneously (Yousefi et al, ). Yousefi et al have proposed another combination method based on ACO algorithm and gossiping algorithm for optimization step in MRF model.…”

Section: Introductionmentioning

confidence: 99%

A novel Markov random field model based on region adjacency graph for T1 magnetic resonance imaging brain segmentation

Ahmadvand

Yousefi

Shalmani

2017

Int J Imaging Syst Tech

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Tissue segmentation in magnetic resonance brain scans is the most critical task in different aspects of brain analysis. Because manual segmentation of brain magnetic resonance imaging (MRI) images is a time-consuming and labor-intensive procedure, automatic image segmentation is widely used for this purpose. As Markov Random Field (MRF) model provides a powerful tool for segmentation of images with a high level of artifacts, it has been considered as a superior method. But because of the high computational cost of MRF, it is not appropriate for online processing. This article has proposed a novel method based on a proper combination of MRF model and watershed algorithm in order to alleviate the MRF's drawbacks. Results illustrate that the proposed method has a good ability in MRI image segmentation, and also decreases the computational time effectively, which is a valuable improvement in the online applications.

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Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms

Yousefi

Azmi

Zahedi

2012

Medical Image Analysis

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Brain tumor segmentation in 3D MRIs using an improved Markov random field model

Cited by 4 publications

References 13 publications

An adaptive sparse Bayesian model combined with joint information-based label fusion for brain tumor segmentation in MRI

An adaptive sparse Bayesian model combined with joint information-based label fusion for brain tumor segmentation in MRI

A novel Markov random field model based on region adjacency graph for T1 magnetic resonance imaging brain segmentation

Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms

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