Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images
Ant Colony Optimization (ACO) is a method of heuristic search using in general artificial intelligence (swarm intelligence) to simulate the behavior of the aggregate food for ants to find new solutions to the combinatorial optimization problems. Artificial ant's behavior depends on the trails o...
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| Format: | Article |
| Language: | English |
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Mosul University
2012-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.mosuljournals.com/article_163719_db1489b79b447782c68f814a0dc9403d.pdf |
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| _version_ | 1850196616758165504 |
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| author | Maha Rahman Hasso Aseel Ali |
| author_facet | Maha Rahman Hasso Aseel Ali |
| author_sort | Maha Rahman Hasso |
| collection | DOAJ |
| description | Ant Colony Optimization (ACO) is a method of heuristic search using in general artificial intelligence (swarm intelligence) to simulate the behavior of the aggregate food for ants to find new solutions to the combinatorial optimization problems. Artificial ant's behavior depends on the trails of real ant with additional capabilities to make it more effective such as a memory to save the past events. Every ant build solutions to the problem, and uses the information grouped about the features and performance of the private problem, to change the look to the ant problem.
In this work, an edge detection technique based on Ant Colony Optimization is used by selecting pheromone matrix which represents the information about edges in each pixel based on the guidelines set up by the ant on the image. Multiple values for different sizes of neighbor pixels are applied and a heuristic information function to test results is proposed. The results show high accuracy in edge detection of different biomedical images with different neighbors, the proposed algorithm is implemented in C Sharp 2008 language which provides high-efficiency software visible language and speed. A comparative study is also given illustrating the superiority of the proposed algorithm. |
| format | Article |
| id | doaj-art-a16436bb274e4fe7b6d8a0961e981d00 |
| institution | OA Journals |
| issn | 1815-4816 2311-7990 |
| language | English |
| publishDate | 2012-12-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj-art-a16436bb274e4fe7b6d8a0961e981d002025-08-20T02:13:24ZengMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902012-12-0192637910.33899/csmj.2012.163719163719Determined the Edges Using the ant Colony Algorithm and Apply them to Medical ImagesMaha Rahman Hasso0Aseel Ali1College of Computer Science and Mathematics University of MosulCollege of Computer Science and Mathematics University of MosulAnt Colony Optimization (ACO) is a method of heuristic search using in general artificial intelligence (swarm intelligence) to simulate the behavior of the aggregate food for ants to find new solutions to the combinatorial optimization problems. Artificial ant's behavior depends on the trails of real ant with additional capabilities to make it more effective such as a memory to save the past events. Every ant build solutions to the problem, and uses the information grouped about the features and performance of the private problem, to change the look to the ant problem. In this work, an edge detection technique based on Ant Colony Optimization is used by selecting pheromone matrix which represents the information about edges in each pixel based on the guidelines set up by the ant on the image. Multiple values for different sizes of neighbor pixels are applied and a heuristic information function to test results is proposed. The results show high accuracy in edge detection of different biomedical images with different neighbors, the proposed algorithm is implemented in C Sharp 2008 language which provides high-efficiency software visible language and speed. A comparative study is also given illustrating the superiority of the proposed algorithm.https://csmj.mosuljournals.com/article_163719_db1489b79b447782c68f814a0dc9403d.pdfant colony optimizationartificial intelligence |
| spellingShingle | Maha Rahman Hasso Aseel Ali Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images Al-Rafidain Journal of Computer Sciences and Mathematics ant colony optimization artificial intelligence |
| title | Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images |
| title_full | Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images |
| title_fullStr | Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images |
| title_full_unstemmed | Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images |
| title_short | Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images |
| title_sort | determined the edges using the ant colony algorithm and apply them to medical images |
| topic | ant colony optimization artificial intelligence |
| url | https://csmj.mosuljournals.com/article_163719_db1489b79b447782c68f814a0dc9403d.pdf |
| work_keys_str_mv | AT maharahmanhasso determinedtheedgesusingtheantcolonyalgorithmandapplythemtomedicalimages AT aseelali determinedtheedgesusingtheantcolonyalgorithmandapplythemtomedicalimages |