期刊
KNOWLEDGE-BASED SYSTEMS
卷 24, 期 8, 页码 1131-1138出版社
ELSEVIER
DOI: 10.1016/j.knosys.2011.02.013
关键词
Image segmentation; Minimum cross entropy; Thresholding; Recursive programming; Genetic algorithms
资金
- National Natural Science Foundation of China [60803049, 60632050]
Image segmentation is one of the most critical tasks in image analysis. Thresholding is definitely one of the most popular segmentation approaches. Among thresholding methods, minimum cross entropy thresholding (MCET) has been widely adopted for its simplicity and the measurement accuracy of the threshold. Although MCET is efficient in the case of bilevel thresholding, it encounters expensive computation when involving multilevel thresholding for exhaustive search on multiple thresholds. In this paper, an improved scheme based on genetic algorithm is presented for fastening threshold selection in multilevel MCET. This scheme uses a recursive programming technique to reduce computational complexity of objective function in multilevel MCET. Then, a genetic algorithm is proposed to search several near-optimal multilevel thresholds. Empirically, the multiple thresholds obtained by our scheme are very close to the optimal ones via exhaustive search. The proposed method was evaluated on various types of images, and the experimental results show the efficiency and the feasibility of the proposed method on the real images. (C) 2011 Elsevier B.V. All rights reserved.
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