Artificial Bee Colony Based Fuzzy Clustering Algorithms For Mri Image Segmentation

AYAT, ALROSAN and NORITA, NORWAWI and WALEED, ALOMOUSH and WEDIAH, ISMAIL (2014) Artificial Bee Colony Based Fuzzy Clustering Algorithms For Mri Image Segmentation. In: International Conference on Advances in Computer Science and Electronics Engineering - CSEE 2014, 08-09 March, 2014, Kuala Lumpur, Malaysia.

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Abstract

Fuzzy clustering algorithms (FCM) have some disadvantage. The main disadvantage is the cluster centroids initialization sensitivity and trapped in local optima. This study proposed a novel clustering method by coupling artificial bee colony with fuzzy c-means (ABC-FCM) algorithm. The technique exploits the superior capabilities of ABC in searching for optimum initial cluster centers and uses these clusters as the input for FCM, thus improving the segmentation of MRI brain images. The performance of the newly developed approach was tested using two sets of MRI images: simulated brain data and real MRI images.

Item Type: Conference or Workshop Item (Paper)
Depositing User: Mr. John Steve
Date Deposited: 13 May 2019 07:48
Last Modified: 13 May 2019 07:48
URI: http://publications.theired.org/id/eprint/2275

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