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

Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

doi:10.1016/j.media.2012.01.001

Cited by 60 publications

(52 citation statements)

References 18 publications

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“…After introductory divisions by FCM, controlling parameters of level sets are evaluated and it additionally advances to discover protest limits and results into strong division. It accomplished great division results to isolate WM, GM tissues in cerebrum MRI and tumour in CT check pictures [6], [25]. Bidirectional Associative Memory (BAM)-typeâ Artificial Neural Network (ANN) based strategy for division and characterization of medicinal pictures, proposed by Sharma, et al [23] performed great even in nearness of commotion and accomplished 100% characterization rate.…”

Section: State-of-the Artmentioning

confidence: 99%

A compressive survey on different image processing techniques to identify the brain tumor

Bhagavan¹,

Imambi²,

Bano³

2018

IJET

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Medical imaging technology has revolutionized health care over the past three decades allowing doctors to detect, cure and improve patient outcomes. Medicinal imaging makes picture of the internal organs, parts, tissues and bones for therapeutic examination and research pur-poses. It can likewise be utilized to think about elements of a few organs. X-ray and CT scanner are the two greatest after-effect of headway of imaging methods supplanting 2D procedures. X-ray is the standout amongst the most critical pre-processing ventures in tumor discovery. Magnetic resonance imaging (MRI) is really an imaging procedure in the restorative field. It is utilized as a part of radiology for imagining interior structures of the body and furthermore how they work. X-ray gives you the 3D picture of the inside bits of the body which enables the specialist to dissect the infection or tumor effortlessly though old imaging procedures like x-beam imaging gives you 2D pictures. In this paper we are introducing distinctive systems for distinguishing, preparing restorative pictures.

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Section: State-of-the Artmentioning

confidence: 99%

A compressive survey on different image processing techniques to identify the brain tumor

Bhagavan¹,

Imambi²,

Bano³

2018

IJET

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“…In this paper, five different well-known classifiers (experts) such as DAQK, NB, MDC (GMM is proper methods for brain tissue segmentation [5,8,10]) and also two different kind of ensemble of weak learners including Bagging and Boosting classifiers, which use 20 weak learner, are used. We used 20 weak classifier to have a good tradeoff between accuracy and time.…”

Section: Comparison Of Different Set Of Individual Classifiers Withinmentioning

confidence: 99%

“…In these methods, MRF algorithm is a post-processing step for smoothing different segments and to remove the noise. Furthermore, Due to the ability of MRF against noise and INU, different methods based on it have been proposed for MRI brain segmentation [10,11]. Yousefi et al and Ouadfel et al [10,12] proposed to use evolutionary algorithm for the optimization step.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Due to the ability of MRF against noise and INU, different methods based on it have been proposed for MRI brain segmentation [10,11]. Yousefi et al and Ouadfel et al [10,12] proposed to use evolutionary algorithm for the optimization step. Ahmadvand et al [13] proposed a preprocessing method for decreasing the runtime of the MRF based method for MR image segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Supervised segmentation of MRI brain images using combination of multiple classifiers

Ahmadvand

Sharififar

Daliri

2015

Australas Phys Eng Sci Med

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Segmentation of different tissues is one of the initial and most critical tasks in different aspects of medical image processing. Manual segmentation of brain images resulted from magnetic resonance imaging is time consuming, so automatic image segmentation is widely used in this area. Ensemble based algorithms are very reliable and generalized methods for classification. In this paper, a supervised method named dynamic classifier selection-dynamic local training local tanimoto index, which is a member of combination of multiple classifiers (CMCs) methods is proposed. The proposed method uses dynamic local training sets instead of a full statics one and also it change the classifier rank criterion properly for brain tissue classification. Selection policy for combining the different decisions is implemented here and the K-nearest neighbor algorithm is used to find the best local classifier. Experimental results show that the proposed method can classify the real datasets of the internet brain segmentation repository better than all single classifiers in ensemble and produces significantly improvement on other CMCs methods.

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“…Finally, this paper has high segmentation success rate. Yousef [2] proposed a new algorithm to split the spinal fluid in the brain's gray matter and white matter. This method mainly divided into two parts.…”

Section: Introductionmentioning

confidence: 99%

The Recognition of Basal Ganglia Area in the Brain MRI Image with Hybrid Classifier

Wu¹,

Chin²,

Chin³

et al. 2017

IJIEE

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Abstract-To understand the relationship between the volume of basal ganglia and parkinson's disease, this paper proposes the recognition of basal ganglia in the brain MRI image with hybrid classifier. It can locate the position of basal ganglia in the 2D brain MRI image. It employs three feature extraction methods to extract the texture feature of basal ganglia. The three feature extraction methods include GLCM, Law's mask and Hu's method .Next, we uses rank search method and genetic search method to perform feature selection. It can reduce computation complexity. Finally, hybrid classifier combining neuro-fuzzy system and adaboost algorithm is used to achieve recognition. From experimental result, we discovered that our proposed method has the high recognition rate.Index Terms-Parkinson's disease, magnetic resonance imaging, basal ganglia, feature pooling, neural-fuzzy-based adaboost.

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

Cited by 60 publications

References 18 publications

A compressive survey on different image processing techniques to identify the brain tumor

A compressive survey on different image processing techniques to identify the brain tumor

Supervised segmentation of MRI brain images using combination of multiple classifiers

The Recognition of Basal Ganglia Area in the Brain MRI Image with Hybrid Classifier

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