Combining information from thresholding techniques through an evolutionary Bayesian network algorithm

Segmentation is an important task in image processing because it could affect the performance of other steps in image analysis. One of the most used methods for segmentation is thresholding which can be formulated as an optimization problem, and evolutionary algorithms (EAs) are alternatives commonly applied to solve it. Estimation of Distribution Algorithms (EDAs) is a branch of EAs that explores the search space by building a probabilistic model, such as Bayesian Networks (BNs). In this article is proposed a BN-based EDA for multilevel image segmentation called BNMTH. The proposed approach iteratively selects the combination of thresholding techniques that permits to find the best configuration of thresholds for a digital image, exploring the inter-dependencies between the decision variables (thresholds) and the different techniques. BNMTH is applied over a set of benchmark images and the results of the segmentation are qualitatively analyzed by using the Peak Signal-to-Noise Ratio (PSNR), the Structure Similarity Index (SSIM) and the Feature Similarity Index (FSIM). Besides, a statistical analysis is provided to compare BNMTH with other state-of-the-art optimization algorithms. The results show that BNMTH is a competitive approach for image segmentation, providing accurate results in almost all the cases. Moreover, the segmented images and the histograms show that the classes are accurately generated even in complex conditions. (C) 2020 Elsevier B.V. All rights reserved.