Improving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithm
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METHODOLOGIES AND APPLICATION
Improving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithm Mohamed Abd Elaziz1 • Uddalok Sarkar2 • Sayan Nag2,3 • Salvador Hinojosa4,5 • Diego Oliva4
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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 TIIFE, 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. Keywords Type II fuzzy entropy Paddy Field Algorithm Plant Propagation Algorithm Multilevel thresholding
1 Introduction Over the last decades, the increased availability of digital cameras has encouraged the development of vision-based systems to solve problems on diverse areas such as
medicine (Mostafa et al. 2017), topology (Miao et al. 2015), agriculture (Riomoros et al. 2010), and surveillance (Bhandari et al. 2015a). Despite the different nature of the applications, most of the systems involve the segmentation of the objects present on the image. As a result, the segmentation of images has become an attractive research topic. One of the most used segmentation methods due to
Communicated by V. Loia. 2
Department of Electrical Engineering, UG-IV Jadavpur University, Kolkata, India
Mohamed Abd Elaziz [email protected]
3
Department of Medical Biophysics, University of Toronto, Toronto, Canada
Uddalok Sarkar [email protected]
4
Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI Av. Revolucio´n 150
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