Entropy-based thresholding techniques are quite popular and effective for image segmentation. Among different entropy-based techniques, minimum cross-entropy thresholding (MCET) has received wide attention in the field of image segmentation. Considering the high time complexity of MCET technique for multilevel thresholding, recursive approach to reducing its computational cost is highly desired. To reduce the complexity, further optimization techniques can be applied to find optimal multilevel threshold values. In this paper, a novel improved particle swarm optimization (IPSO)-based multilevel thresholding algorithm is proposed to search the near-optimal MCET thresholds. The general PSO algorithm often suffers from premature convergence problem which has been addressed in the IPSO by decomposing a high-dimensional swarm into several one-dimensional swarms, and then premature convergence is removed from each one-dimensional swarm. The proposed technique is applied to the set of grayscale images, and the experimental results infer that it produces better MCET optimal threshold values at a higher and faster convergence rate. The qualitative and quantitative results are compared with existing optimization techniques like modified artificial bee colony, Cuckoo search, Firefly, particle swarm optimization, and genetic algorithm. It has been observed that the proposed technique performs better in terms of producing better fitness value, less CPU time as quantitative measurements, and effective misclassification error, peak signal-to-noise ratio, feature similarity index measurement, complex wavelet structural similarity index measurement values as qualitative measurements compared to other considered state-of-the-art methods.
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机译：基于熵的阈值技术非常流行且有效地对图像分割。在基于熵的技术中，最小跨熵阈值（MCET）已经在图像分割领域中得到了广泛的注意。考虑到MCET技术对多级阈值的高时间复杂性，非常需要递归方法来降低其计算成本。为了降低复杂性，可以应用进一步的优化技术来查找最佳的多级阈值。本文提出了一种新颖的改进的粒子群优化（IPSO）的多级阈值阈值算法，用于搜索近最优的MCET阈值。 General PSO算法往往遭受过早的收敛问题，通过将高维群分解为几维群，然后从每个一维群中移除过早的收敛。所提出的技术应用于该组灰度图像，并且实验结果推断它以更高且更快的收敛速率产生更好的MCET最佳阈值。将定性和定量结果与现有的优化技术进行比较，如修改的人造蜂殖民地，杜鹃搜索，萤火虫，粒子群优化和遗传算法。已经观察到所提出的技术在产生更好的健康价值，较少的CPU时间作为定量测量，以及有效的错误分类误差，峰值信噪比，特征相似度指数测量，复杂小波结构相似度指标测量值的方面更好与其他考虑的最先进的方法相比，定性测量。
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