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Journal of Applied Sciences
  Year: 2015 | Volume: 15 | Issue: 1 | Page No.: 100-109
DOI: 10.3923/jas.2015.100.109
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MRI Brain Segmentation Using a Hybrid Artificial Bee Colony Algorithm with Fuzzy-C Mean Algorithm

Mutasem K. Alsmadi

In the field of medical image processing, image segmentation plays important role in extracting significant and reliable features in order to determine the tumor regions in the Magnetic Resonance Images (MRI). Where brain image segmentation is considered as an interesting and difficult issue in this field. In this study a new automatic and intelligent clustering approach is proposed for the segmentation of brain tumor using the hybridization of Fuzzy C-mean and Artificial Bee Colony algorithms (FCMABC), in order to enhance the ability of the FCM to segment the MRI brain image, extract the appropriate number of cluster centres (tumor region) and the number of abnormal cells (multiple sclerosis lesions) in each cluster using automatic and dynamic way. A comparative analysis was conducted between the proposed algorithm and traditional FCM. The experimental results showed the efficiency of the proposed FCMABC in improving the performance of traditional FCM in terms of the clustering accuracy. Moreover, the proposed algorithm is more robust and effective against noise, when compared with the traditional FCM.
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  •    Segmentation of MRI Brain Images Using FCM Improved by Firefly Algorithms
How to cite this article:

Mutasem K. Alsmadi , 2015. MRI Brain Segmentation Using a Hybrid Artificial Bee Colony Algorithm with Fuzzy-C Mean Algorithm. Journal of Applied Sciences, 15: 100-109.

DOI: 10.3923/jas.2015.100.109


07 December, 2016
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