Multilevel Image Thresholding Using a Fully Informed Cuckoo Search Algorithm
This work addresses computational efficiency in image segmentation for applications like medical imaging, though it is incremental as it builds on existing metaheuristic approaches.
The paper tackled the computational expense of conventional multilevel image thresholding by improving the cuckoo search algorithm with a fully informed strategy, resulting in a method that is more accurate and efficient than four other popular methods.
Though effective in the segmentation, conventional multilevel thresholding methods are computationally expensive as exhaustive search are used for optimal thresholds to optimize the objective functions. To overcome this problem, population-based metaheuristic algorithms are widely used to improve the searching capacity. In this paper, we improve a popular metaheuristic called cuckoo search using a ring topology based fully informed strategy. In this strategy, each individual in the population learns from its neighborhoods to improve the cooperation of the population and the learning efficiency. Best solution or best fitness value can be obtained from the initial random threshold values, whose quality is evaluated by the correlation function. Experimental results have been examined on various numbers of thresholds. The results demonstrate that the proposed algorithm is more accurate and efficient than other four popular methods.