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Abstract
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.
Highlights
Thresholding is a popular method for image segmentation
Fan et al [7] approximated the histogram with a mixed Gaussian model, and estimated the parameters with an hybrid algorithm based on particle swarm optimization and expectation maximization
We compared the maximum Tsallis entropy thresholding (MTT) to other criteria including maximum between class variance thresholding (MBCVT), maximum entropy thresholding (MET), and minimum cross entropy thresholding (MCET)
Summary
Thresholding is a popular method for image segmentation. From a grayscale image, bi-level thresholding can be used to create binary images, while multilevel thresholding determines multiple thresholds which divide the pixels into multiple groups. Nakib et al [18] proposed a new image thresholding model based on multi-objective optimization, and used an improved genetic algorithm to solve the model. Maitra et al [19] proposed an improved variant of particle swarm optimization (PSO) for the task of image multilevel thresholding. Both GA and PSO can jump from local minima, but their efficiencies are not satisfactory.
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