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ASME Press Select Proceedings
International Conference on Computer and Automation Engineering, 4th (ICCAE 2012)
Jianhong Zhou
Jianhong Zhou
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Segmentation of medical images, particularly magnetic resonance images of brain is complex and it is considered as a huge challenge in image processing. Among the numerous algorithms presented in this context, the fuzzy Cmean (FCM) algorithm is widely used in MR images segmentation. Recently, researchers have introduced two new parameters in order to improve the performance of FCM algorithm, which are calculated using neural network in a complex and time consuming manner. These two parameters have been then calculated by other researchers using genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, which although it has reduced the time but no change obtained in the resulted segmentation quality. In this paper we calculate these two parameters using the artificial bee colony (ABC) algorithm aiming to both reduce the time and to reach a higher quality than that obtained by previous reports.

Key Words
1 Introduction
2. Improvement FCM Clustering Algorithm
3. New Optimization Based on Artificial Bee Colony (ABC) Algorithm:
4. Experimental Results:
5. Conclusion:
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