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

• Published in: Medical image analysis Vol. 16; no. 4; pp. 840 - 848
• Main Authors: Yousefi, Sahar, Azmi, Reza, Zahedi, Morteza
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2012
• Subjects: Algorithms; Ant colony optimization (ACO); Brain - anatomy & histology; Brain segmentation; Data Interpretation, Statistical; Gossiping algorithm; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic resonance image (MRI); Magnetic Resonance Imaging - methods; Markov Chains; Markov random field (MRF); Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Brain segmentation; Gossiping algorithm; Markov random field (MRF); Magnetic resonance image (MRI); Ant colony optimization (ACO)
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2012.01.001

Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a so time-consuming task which engages valuable human resources, automatic MRI segmentation have been received an enormous amount of attention. For this purpose various techniques have been applied however Markov Random Field (MRF) based algorithms have produced better results in noisy images compared to other methods. MRF seeks for a label field which minimizes energy function. Traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason MRFs never be used in real time processing environments. This paper proposed a novel method based on MRF and hybrid of social algorithms contains ant colony optimization (ACO) and Gossiping algorithm. Combining ACO with Gossiping algorithm assists ants to find the better path using the information of their neighbors. Therefore, this interaction causes the algorithm converges to optimum solution faster. Several experiments on phantom and real images were performed. Results indicate the proposed algorithm outperforms the traditional MRF in speed and accuracy. [Display omitted] ► This article presents a novel unsupervised MRF-based model for MRI segmentation. ► The proposed method uses ACO and gossiping algorithm to speed up the classic MRF. ► Tackling Gossiping algorithm, the proposed method assists ants in smart decision. ► The IBSR and Brainweb datasets have been used for testing the method. ► Results demonstrate the novel method outperforms other models in speed and quality. Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy.