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

Mathematics

Jorge Munoz-Minjares¹  Osbaldo Vite-Chavez²  Jorge Flores-Troncoso²  Jorge M. Cruz-Duarte³ 

[1] Department of Electrical Engineering, Universidad Autónoma de Zacatecas “Campus Jalpa”, Libramiento Jalpa Km. 156+380, Zacatecas 99601, Mexico;Department of Electrical Engineering, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde 801, Zacatecas 98000, Mexico;Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico;

关键词: image segmentation;    thresholding;    cuckoo search;    generalized Gaussian distribution;   

DOI  :  10.3390/math9182287

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown