Multi-Level Minimum Cross Entropy Thresholding Using Gamma Distribution

Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding has been widely adopted. Most minimum cross entropy thresholding methods use Gaussian distribution as an ideal reference histogram for the images to be thresholded. Clearly, it is doubtful that any natural images would generate a histogram with such a distribution. In this paper, a new minimum cross entropy thresholding method using Gamma distribution is proposed, since it is more general than other distributions. The new entropy thresholding method using Gamma distribution is extended to multi-level thresholding. The experimental results manifest that the proposed method can derive multiple thresholds which are very close to the optimal ones. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for different real time applications.