Journal
ENTROPY
Volume 13, Issue 4, Pages 841-859Publisher
MDPI AG
DOI: 10.3390/e13040841
Keywords
image segmentation; multi-level thresholding; maximum Tsallis entropy; artificial bee colony
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Funding
- National Natural Science Foundation of China [60872075]
- National Technical Innovation Project Essential Project Cultivate Project [706928]
- Nature Science Fund in Jiangsu Province [BK2007103]
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This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificial bee colony approach since execution of an exhaustive algorithm would be too time-consuming. The experiments demonstrate that: 1) the Tsallis entropy is superior to traditional maximum entropy thresholding, maximum between class variance thresholding, and minimum cross entropy thresholding; 2) the artificial bee colony is more rapid than either genetic algorithm or particle swarm optimization. Therefore, our approach is effective and rapid.
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