A multilevel biomedical image thresholding approach using the chaotic modified cuckoo search

This article addresses this challenge and proposes a novel approach based on the modified cuckoo search and chaos theory. This article describes a novel approach for multilevel biomedical image segmentation based on the modified cuckoo search and chaos theory which is the major contribution to the literature. The modified cuckoo search approach helps to model the Levy flight efficiently and the incorporation of the chaos theory helps to maintain the diversity in the population. The proposed approach helps to determine the optimal threshold values for a given number of thresholds. Four different objective functions are used to get the realistic segmented output which is essential in biomedical image analysis. Moreover, detailed analysis is also helpful in understanding the suitable objective function for biomedical image segmentation. This work also helps to choose suitable chaotic maps with different optimization algorithms. Hybridization of chaos theory and modified cuckoo search helps to overcome the local optima and to find the global optima cost-effectively. The chaos theory is incorporated in this proposed work to replace some solutions with some chaotic sequences to enhance the associated randomness with various phases which is beneficial to overcome the premature convergence and its related issues. The optimum setup is determined by investigating the effect of different chaotic maps along with some standard metaheuristic optimization approaches. Both qualitative and quantitative approaches are used to evaluate and compare the proposed approach. The proposed algorithm is compared with four state-of-the-art approaches. The obtained results clearly show that the proposed approach outperforms some state-of-the-art approaches in terms of both quantitative results and segmented output. On average, the proposed approach can achieve 255.8331 MSE, 24.07047 PSNR, 291.6077 mean, 0.038869 SD, 0.688655 SSIM, and 16.05358 s execution time (all for nine clusters). It can be observed that the proposed approach can determine the optimal set of clusters comparatively faster on most occasions, especially for the higher number of clusters.