Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images

Abstract

The detection of chest CT scan images of the lung play a key role in clinical decision making for some lung disease, such as tumors, pulmonary tuberculosis, solitary pulmonary nodule, lung masses and so on. In this paper, a novel automated CT scan image segmentation algorithm based on hybrid Ant Colony algorithm and snake algorithm is proposed. Firstly, traditional snake algorithm is used to detect the possible edge points of focal of a disease. Then Ant Colony Optimization (ACO) algorithm is applied to search the possible edge points of focal of a disease repeatedly. Finally, real edges can be extracted according to the intensity of pheromones. Simulation experiment results demonstrate that the proposed algorithm is more efficient and effective than the methods we compared it to.