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

Ahmadvand, Ali; Yousefi, Sahar; Shalmani, Mohammad Taghi Manzuri

doi:10.1002/ima.22212

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…The FCM + PRE + MRF method provides results with the less execution time (reduced by a mean of 89.3%) compared to FCM + MRF method. The novel combination of MRF and watershed methods for MRI brain image segmentation is proposed . It is a two step process runs sequentially, such that runtime does not increase much.…”

Section: Resultsmentioning

confidence: 99%

MRI brain segmentation in combination of clustering methods with Markov random field

Saladi

Prabha

2018

Int J Imaging Syst Tech

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Medical image segmentation is a preliminary stage of inclusion in identification tools. The correct segmentation of brain Magnetic Resonance Imaging (MRI) images is crucial for an accurate detection of the disease diagnosis. Due to in‐homogeneity, low distinction and noise the segmentation of the brain MRI images is treated as the most challenging task. In this article, we proposed hybrid segmentation, by combining the clustering methods with Hidden Markov Random Field (HMRF) technique. This aims to decrease the computational load and improves the runtime of segmentation method, as MRF methodology is used in post‐processing the images. Its evaluation has performed on real imaging data, resulting in the classification of brain tissues with dice similarity metric. These results indicate the improvement in performance of the proposed method with various noise levels, compared with existing algorithms. In implementation, selection of clustering method provides better results in the segmentation of MRI brain images.

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Section: Resultsmentioning

confidence: 99%

MRI brain segmentation in combination of clustering methods with Markov random field

Saladi

Prabha

2018

Int J Imaging Syst Tech

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“…The research work, proposed Optimal Region Amalgamation Technique (RAT) to overcome the over-segmentation problem occurred in watershed method, first it presents a Region Merging Method (RMM) based on applying the Markov random field(MRF) model on the RAG(Region Adjacency Graph) [14][15] [16] to improve the quality of watershed method. The relationship of inter-region similarities presents in tumor regions is then performed by watershed method in involving the spatial domain and clustering technique in feature space into image mapping in order to determine Optimal RAT.…”

Section: Proposed Methodologymentioning

confidence: 99%

Robust and Accurate Automated Methods for Detection and Segmentation of Brain Tumor in MRI

Bhima*,

Jagan

2019

IJRTE

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In this proposed study, a novel Multimodal brain MR image segmentation method is presented to overcome the unattractive and undesirable over segmentation characteristics of conventional Watershed method. The proposed work, presents Optimal Region Amalgamation Technique (RAT) that merge the Watershed method (spatial domain) and Fuzzy C-means clustering (feature spaces) to reduce the unattractive and undesirable over segmentation in brain MR images. In the proposed work, to improve the quality of segmentation results of Watershed method, initially it construct a RAG(Region Merging Graph) for optimal RAT by applying the most popular MRF(Markov Random Field) method . Consequently, the inter-region comparison is presented by applying the watershed method in Spatial Domain and Fuzzy C-Means clustering method in Feature Space for image mapping to compute the Optimal Region Amalgamation. Further, to determine the Feature space and domain space illustration of the brain MR image segmentation, the SGD (Spatial Graph Depiction) is presented that is computed with FSD (Feature Space Depiction) which is obtained by watershed partitioning and FCM clustering method. The experimental results on multimodal brain MR image datasets presents that the proposed novel Optimal Region Amalgamation Technique (RAT) exhibits more promising MR images segmentation results with compared to the traditional watershed method. Finally, an assessment and evaluation of the state-of-the-art brain tumor segmentation methods are presented and future directions to improve and standardize the detection and segmentation of brain tumor into daily clinical treatment are addressed.

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“…The Hidden Markov Random Field Model is a segmentation algorithm popularly used in image segmentation, such as by Ahmadvand et al [10], Jianhua et al [11], Shah Saurabh [12], and Mingsheng Chen [13]. A combination of fuzzy clustering and the MRF model was presented by Mingsheng Chen et al, where the Fuzzy C-Means (FCM) algorithm was combined with the MRF model to filter the effect of noise and to increase the integrity of segmented regions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm

Daoudi,

Mahmoudi

2024

Computers

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The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9.

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

Cited by 9 publications

References 36 publications

MRI brain segmentation in combination of clustering methods with Markov random field

MRI brain segmentation in combination of clustering methods with Markov random field

Robust and Accurate Automated Methods for Detection and Segmentation of Brain Tumor in MRI

Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm

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