Improving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithm

The segmentation of digital images is an open problem that has increasingly attracted the attention of researchers during the last years. Thresholding approaches are often used due to their independence from the resolution of the images and their speed. However, simple thresholding approaches usually generate low-quality images. To achieve a better balance between speed and quality, many criteria are used to select the thresholds that segment the image. The type II fuzzy entropy (TII-FE) was introduced to perform image thresholding by modeling the classes of an image as membership functions to avoid uncertainty on the selection of the thresholds leading to improvement regarding the quality of the segmented image. To maximize the TII-FE, an efficient optimizer should be used to converge quickly to the optimal. In this paper, a hybrid method based on the Paddy Field Algorithm (PFA) and the Plant Propagation Algorithm (PPA) with the disruption operator (HPFPPA-D) is presented for the maximization of the TII-FE. The hybridization of these algorithms is used to enhance the performance of each algorithm by introducing operators from other approaches. In this case, the PFA shows good exploitation features that are complemented by the exploration behavior of PPA and refined with the disruption operator. The synergy between those methods has led to an accurate methodology for TII-FE thresholding. The proposed HPFPPA-D for TII-FE is evaluated using a set of benchmark images regarding convergence and image quality. The results are compared against other state-of-the-art evolutionary algorithms providing evidence of a superior and significant performance.