An Improved PSO-Based Multilevel Image Segmentation Technique Using Minimum Cross-Entropy Thresholding

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.