Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians
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 thi...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/18/2287 |
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| author | Jorge Munoz-Minjares Osbaldo Vite-Chavez Jorge Flores-Troncoso Jorge M. Cruz-Duarte |
| author_facet | Jorge Munoz-Minjares Osbaldo Vite-Chavez Jorge Flores-Troncoso Jorge M. Cruz-Duarte |
| author_sort | Jorge Munoz-Minjares |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:28:24Z |
| format | Article |
| id | doaj.art-cfac7ed97b3f4deb8f52fd4c4f1dec76 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T07:28:24Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-cfac7ed97b3f4deb8f52fd4c4f1dec762023-11-22T14:06:08ZengMDPI AGMathematics2227-73902021-09-01918228710.3390/math9182287Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized GaussiansJorge Munoz-Minjares0Osbaldo Vite-Chavez1Jorge Flores-Troncoso2Jorge M. Cruz-Duarte3Department of Electrical Engineering, Universidad Autónoma de Zacatecas “Campus Jalpa”, Libramiento Jalpa Km. 156+380, Zacatecas 99601, MexicoDepartment of Electrical Engineering, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde 801, Zacatecas 98000, MexicoDepartment of Electrical Engineering, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde 801, Zacatecas 98000, MexicoTecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, MexicoObject 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.https://www.mdpi.com/2227-7390/9/18/2287image segmentationthresholdingcuckoo searchgeneralized Gaussian distribution |
| spellingShingle | Jorge Munoz-Minjares Osbaldo Vite-Chavez Jorge Flores-Troncoso Jorge M. Cruz-Duarte Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians Mathematics image segmentation thresholding cuckoo search generalized Gaussian distribution |
| title | Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians |
| title_full | Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians |
| title_fullStr | Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians |
| title_full_unstemmed | Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians |
| title_short | Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians |
| title_sort | alternative thresholding technique for image segmentation based on cuckoo search and generalized gaussians |
| topic | image segmentation thresholding cuckoo search generalized Gaussian distribution |
| url | https://www.mdpi.com/2227-7390/9/18/2287 |
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