Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

Jorge Munoz-Minjares, Osbaldo Vite-Chavez, Jorge Flores-Troncoso and Jorge M. Cruz-Duarte
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Jorge Munoz-Minjares: Department of Electrical Engineering, Universidad Autónoma de Zacatecas “Campus Jalpa”, Libramiento Jalpa Km. 156+380, Zacatecas 99601, Mexico
Osbaldo Vite-Chavez: Department of Electrical Engineering, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde 801, Zacatecas 98000, Mexico
Jorge Flores-Troncoso: Department of Electrical Engineering, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde 801, Zacatecas 98000, Mexico
Jorge M. Cruz-Duarte: Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico

Mathematics, 2021, vol. 9, issue 18, 1-19

Abstract: Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

Keywords: image segmentation; thresholding; cuckoo search; generalized Gaussian distribution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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