Home >> Archive >> Vol:1, No:2 >> Mini Review: Nature-Inspired Algorithms in Tomography


Mini Review: Nature-Inspired Algorithms in Tomography

| Çağlar Cengizler |


Year:2022| Vol:1| No:2| PP 87-93

Abstract
Tomography is simply generation of cross-sectional images of body via any kind of penetrating wave. Today, tomography is one of the most popular medical imaging modalities that is mostly preferred for monitoring body internals to search for any kind of abnormalities. In this article, it is aimed to review some of the most successful implementations of nature-inspired algorithms used in the development of tomography technology.

Keywords
Tomography; Intelligent; Nature-inspired; Technology
Full Paper (PDF)

Rights and permissions
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References
  1. Ambrose, J. Computerized transverse axial tomography. In Brit J Radiol, 46: 147-150, 1973.
  2. Hounsfield, G. N Computerized transverse axial scanning (tomography): Part 1. Description of system. In The British journal of radiology, 46 (552): 1016-1022, 1973.
  3. Fleischmann, D. and Boas, F E. Computed tomography—old ideas and new technology. In European radiology, 21 (3): 510-517, 2011.
  4. Rattan, S.; Kaur, S.; Kansal, N. and Kaur, J. An optimized lung cancer classification system for computed tomography images. In 2017 Fourth International Conference on Image Information Processing (ICIIP), pages 1-6, 2017.
  5. Yang, X-S. and Gandomi, A. H. Bat algorithm: a novel approach for global engineering optimization. In Engineering computations, 2012.
  6. de Carvalho Filho, A. O.; Silva, A. C.; Cardoso de Paiva, A.; Nunes, R. A. and Gattass, M. Computer-aided diagnosis of lung nodules in computed tomography by using phylogenetic diversity, genetic algorithm, and SVM. In Journal of digital imaging, 30 (6): 812-822, 2017.
  7. Mirjalili, S. Genetic algorithm. In Evolutionary algorithms and neural networks, pages 43-55, Springer, 2019.
  8. Kapli, P.; Yang, Z. and Telford, M. J Phylogenetic tree building in the genomic age. In Nature Reviews Genetics, 21 (7): 428-444, 2020.
  9. Yu, H.; Liu, D.; Shi, H.; Yu, H.; Wang, Z.; Wang, X.; Cross, B.; Bramler, M. and Huang, T. S Computed tomography super-resolution using convolutional neural networks. In 2017 IEEE International Conference on Image Processing (ICIP), pages 3944-3948, 2017.
  10. Samadiani, N. and Moameri, S. Diagnosis of Coronary Artery Disease using Cuckoo Search and genetic algorithm in single photon emision computed tomography images. In 2017 7th International Conference on Computer and Knowledge Engineering (ICCKE), pages 314-318, 2017.
  11. Yang, X-S. and Deb, S. Cuckoo search via L'evy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC), pages 210-214, 2009.
  12. Wang, G-G.; Deb, S. and Cui, Z. Monarch butterfly optimization. In Neural computing and applications, 31 (7): 1995-2014, 2019.
  13. Pleszczy'nski, M.; Zielonka, A.; Polap, D.; Wo'zniak, M. and Ma'ndziuk, J. Polar Bear Optimization For Industrial Computed Tomography With Incomplete Data. In 2021 IEEE Congress on Evolutionary Computation (CEC), pages 681-687, 2021.
  14. Polap, D. and Wo'zniak, M. Polar bear optimization algorithm: Meta-heuristic with fast population movement and dynamic birth and death mechanism. In Symmetry, 9 (10): 203, 2017.
  15. Mishra, R.; Singh, A. and Bajpai, M. K. Self-guided Genetic Algorithm for Limited View Tomography. In 2021 IEEE International Conference on Imaging Systems and Techniques (IST), pages 1-6, 2021.
  16. Chen, S-H.; Chang, P-C.; Cheng, T. and Zhang, Q. A self-guided genetic algorithm for permutation flowshop scheduling problems. In Computers & operations research, 39 (7): 1450-1457, 2012.