The Use of Simple Cellular Automata in Image Processing

  • Laura Dioșan Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
  • Anca Andreica Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
  • Alina Enescu Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania


Cellular Automata have been considered for a series of applications among which several image processing tasks. The goal of this paper is to investigate such existing methods, supporting the broader goal of identifying Cellular Automata rules able to automatically segment images. With the same broader goal in mind as future work, a detailed description of evaluation metrics used for image segmentation is also given in this paper.


[1] Ryan A Beasley. Semiautonomous medical image segmentation using seeded cellular automaton plus edge detector. ISRN Signal Processing, 2012:1–9, 2012.
[2] Lei Bi, Jinman Kim, Lingfeng Wen, A. Kumar, M. Fulham, and D.D. Feng. Cellular automata and anisotropic diffusion filter based interactive tumor segmentation for positron emission tomography. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 5453–5456,
[3] Yu Chen, Zhuangzhi Yan, and Yungao Chu. Cellular automata based level set method for image segmentation. In Complex Medical Engineering, 2007. CME 2007. IEEE/ICME International Conference on, pages 171–174, May 2007.
[4] C. Chira, A. Gog, R. I. Lung, and D. Iclanzan. Complex systems and cellular automata models in the study of complexity. Studia Informatica series, LV:33–49, 2010.
[5] Pabitra Pal Choudhury, Birendra Kumar Nayak, Sudhakar Sahoo, and Sunil Pankaj Rath. Theory and applications of two-dimensional, null-boundary, nine-neighborhood, cellular automata linear rules. CoRR, abs/0804.2346, 2008.
[6] C. Callins Christiyana, V. Rajamani, and A. Usha Devi. Article: Ultra sound kidney image retrieval using time efficient one dimensional glcm texture feature. IJCA Special Issue on Advanced Computing and Communication Technologies for HPC Applications, ACCTHPCA(4):12–17, July 2012. Full text available.
[7] Lee R. Dice. Measures of the amount of ecologic association between species. Ecology, 26(3):297–302, 1945.
[8] Manoj Diwakar, Pawan Kumar Patel, and Kunal Gupta. Cellular automata based edge-detection for brain tumor. In ICACCI, pages 53–59. IEEE, 2013.
[9] Safia Djemame and Mohamed Batouche. Combining cellular automata and particle swarm optimization for edge detection. (14/):16–22, 2012.
[10] Martin Gardner. The fantastic combinations of john conway’s new solitaire game ”life”. Scientific American, 223(10):120–123, October 1970.
[11] Payel Ghosh, Sameer Antani, L. Rodney Long, and George R. Thoma. Unsupervised grow-cut: Cellular automata-based medical image segmentation. In HISB, pages 40–47. IEEE, 2011.
[12] Andac Hamamci, Nadir Kucuk, Kutlay Karaman, Kayihan Engin, and Gözde B. Unal. Tumor-cut: Segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans. Med. Imaging, 31(3):790–804, 2012.
[13] Hugues Juille and Jordan B. Pollack. Coevolving the ideal trainer: Application to the discovery of cellular automata rules. In John R. Koza, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors, Genetic Programming 1998:
Proceedings of the Third Annual Conference, pages 519–527. Morgan Kaufmann, 22-25 July 1998.
[14] Claude Kauffmann and Nicolas Piche. Seeded nd medical image segmentation by cellular automaton on gpu. Int. J. Computer Assisted Radiology and Surgery, 5(3):251–262, 2010.
[15] Okba KAZAR and Sihem SLATNIA. Evolutionary cellular automata for image segmentation and noise filtering using genetic algorithms. Journal of Applied Computer Science & Mathematics, 11(5):33–40, 2011.
[16] A.C.J. Korte and H.J.H. Brouwers. A cellular automata approach to chemical reactions; 1 reaction controlled systems. Chemical Engineering Journal, 228:172–178, 2013.
[17] Yan Liu, H. D. Cheng, Jianhua Huang, Yingtao Zhang, and Xianglong Tang. An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle. J. Digital Imaging, 25(5):580–590, 2012.
[18] D. R. Martin, C. C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, pages II: 416–423, 2001.
[19] Melanie Mitchell, Michael D. Thomure, and Nathan L. Williams. The role of space in the success of coevolutionary learning. In Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems, pages 118–124. MIT Press, 2006.
[20] Jahangir Mohammed and Deepak Ranjan Nayak. An efficient edge detection technique by two dimensional rectangular cellular automata. CoRR, abs/1312.6370, 2013.
[21] Deepak Ranjan Nayak, Prashanta Kumar Patra, and Amitav Mahapatra. A survey on two dimensional cellular automata and its application in image processing. IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication (ETCC-2014), ETCC(1):78–87, 2014.
[22] Deepak Ranjan Nayak, Sumit Kumar Sahu, and Jahangir Mohammed. A cellular automata based optimal edge detection technique using twenty-five neighborhood model. CoRR, abs/1402.1348, 2014.
[23] Gina M. B. Oliveira, Luiz G. A. Martins, Laura B. de Carvalho, and Enrique Fynn. Some investigations about synchronization and density classification tasks in one-dimensional and two-dimensional cellular automata rule spaces. Electr. Notes Theor. Comput. Sci., 252:121–142, 2009.
[24] Blanca Priego, Daniel Souto, Francisco Bellas, and Richard J. Duro. Hyperspectral image segmentation through evolved cellular automata. Pattern Recognition Letters, 34(14):1648–1658, 2013.
[25] Fasel Qadir, Peer M. A., and Khan K. A. Efficient edge detection methods for diagnosis of lung cancer based on two dimensional cellular automata. Advances in Applied Science Research, 4(3):2050–2058, 2012.
[26] R. S. RajKumar and G. Niranjana. Image segmentation and classification of MRI brain tumor based on cellular automata and neural networks. International Journal of Research in Engineering & Advanced Technology, 1(1):1–7, 2013.
[27] P. L. Rosin. Training cellular automata for image processing. In SCIA, pages 195–204, 2005.
[28] D. Safia and B.M. Chawki. Image segmentation using an emergent complex system: Cellular automata. In Systems, Signal Processing and their Applications (WOSSPA), 2011 7th International Workshop on, pages 207–210, May 2011.
[29] D. Safia, D. Oussama, and B.M. Chawki. Image segmentation using continuous cellular automata. In Programming and Systems (ISPS), 2011 10th International Symposium on, pages 94–99, April 2011.
[30] Mohamed Sandeli and Mohamed Batouche. Multilevel thresholding for image segmentation based on parallel distributed optimization. In SoCPaR, pages 134–139. IEEE, 2014.
[31] S. Sato and H. Kanoh. Evolutionary design of edge detector using rule-changing cellular automata. In Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on, pages 60–65, Dec 2010.
[32] Sihem Slatnia, Mohamed Batouche, and Kamal E. Melkemi. Evolutionary cellular automata based-approach for edge detection. In Francesco Masulli, Sushmita Mitra, and Gabriella Pasi, editors, Applications of Fuzzy Sets Theory, 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007, Proceedings, volume 4578 of Lecture Notes in Computer Science, pages 404–411. Springer, 2007.
[33] Sihem Slatnia and Okba Kazar. Evolutionary cellular automata based-approach for region detection., 2015.
[34] Marco Tomassini and Mattias Venzi. Evolution of asynchronous cellular automata for the density task. In Juan J. Merelo Guervs, Panagiotis Adamidis, Hans-Georg Beyer, Jos Luis Fernndez-Villacaas Martn, and Hans-Paul Schwefel, editors, PPSN, volume 2439 of Lecture Notes in Computer Science, pages 934–944. Springer, 2002.
[35] Blanca Maria Priego Torres, Daniel Souto, Francisco Bellas, and Richard J. Duro. Unsupervised segmentation of hyperspectral images through evolved cellular automata. In Manuel Graa, Carlos Toro, Jorge Posada, Robert J. Howlett, and Lakhmi C. Jain, editors, KES, volume 243 of Frontiers in Artificial Intelligence and Applications, pages 2160–2169. IOS Press, 2012.
[36] R. Unnikrishnan, C. Pantofaru, and M. Hebert. Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence, 29(6):929–944, June 2007.
[37] Vladimir Vezhnevets and Vadim Konouchine. Growcut” - interactive multi-label N-D image segmentation by cellular automata. pages 1–7. Russian Academy of Sciences, 2005.
[38] Sartra Wongthanavasu. Cellular Automata for Medical Image Processing. Cellular Automata - Innovative Modelling for Science and Engineering. INTECH Open Access Publisher, unknown 2011.
How to Cite
DIOȘAN, Laura; ANDREICA, Anca; ENESCU, Alina. The Use of Simple Cellular Automata in Image Processing. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 62, n. 1, p. 5-14, may 2017. ISSN 2065-9601. Available at: <>. Date accessed: 29 nov. 2020. doi: