{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:36:36Z","timestamp":1764174996119,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["P37_245"],"award-info":[{"award-number":["P37_245"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a specialized detector that is optimized for the image particularities can help improve edge detection performance. In this paper, we apply transfer learning to optimize cellular automata (CA) rules for edge detection using particle swarm optimization (PSO). Cellular automata provide fast computation, while rule optimization provides adaptability to the properties of the target images. We use transfer learning from synthetic to medical images because expert-annotated medical data is typically difficult to obtain. We show that our method is tunable for medical images with different properties, and we show that, for more difficult edge detection tasks, batch optimization can be used to boost the quality of the edges. Our method is suitable for the identification of structures, such as cardiac cavities on medical images, and could be used as a component of an automatic radiology decision support tool.<\/jats:p>","DOI":"10.3390\/e23040414","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T05:57:18Z","timestamp":1617170238000},"page":"414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization"],"prefix":"10.3390","volume":"23","author":[{"given":"Delia","family":"Dumitru","sequence":"first","affiliation":[{"name":"IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Dio\u0219an","sequence":"additional","affiliation":[{"name":"IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania"},{"name":"Faculty of Mathematics and Computer Science, Babe\u0219\u2013Bolyai University, 400084 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anca","family":"Andreica","sequence":"additional","affiliation":[{"name":"IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania"},{"name":"Faculty of Mathematics and Computer Science, Babe\u0219\u2013Bolyai University, 400084 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8598-2784","authenticated-orcid":false,"given":"Zolt\u00e1n","family":"B\u00e1lint","sequence":"additional","affiliation":[{"name":"IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania"},{"name":"Faculty of Physics, Babe\u0219\u2013Bolyai University, 400084 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, R. (2016). Edge Detection Using Convolutional Neural Network, Springer.","DOI":"10.1007\/978-3-319-40663-3_2"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/S0898-1221(97)00021-7","article-title":"VLSI architecture of a cellular automata machine","volume":"33","author":"Khan","year":"1997","journal-title":"Comput. Math. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.24193\/subbi.2017.2.01","article-title":"The Use of Simple Cellular Automata in Image Processing","volume":"62","author":"Andreica","year":"2017","journal-title":"Stud. Univ. Babes-Bolyai Inform."},{"key":"ref_4","unstructured":"Schiff, J.L. (2011). Cellular Automata: A Discrete View of the World (Wiley Series in Discrete Mathematics & Optimization), John Wiley & Sons."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mohammed, J., and Nayak, D.R. (2014, January 27\u201328). An efficient edge detection technique by two dimensional rectangular cellular automata. Proceedings of the International Conference on Information Communication and Embedded Systems (ICICES2014), Chennai, India.","DOI":"10.1109\/ICICES.2014.7033847"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Angulo, K., Gil, D., and Espitia, H. (2020). Method for Edges Detection in Digital Images through the Use of Cellular Automata, Springer.","DOI":"10.1007\/978-3-030-33614-1_1"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"M\u0103rginean, R., Andreica, A., Dio\u015fan, L., and B\u00e1lint, Z. (2020). Butterfly Effect in Chaotic Image Segmentation. Entropy, 22.","DOI":"10.3390\/e22091028"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.neucom.2016.05.092","article-title":"An edge detection method using outer Totalistic Cellular Automata","volume":"214","author":"Amrogowicz","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1016\/j.aeue.2015.05.010","article-title":"Cellular edge detection: Combining cellular automata and cellular learning automata","volume":"69","author":"Mohammad","year":"2015","journal-title":"Int. J. Electron. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.compeleceng.2015.01.017","article-title":"Edge detection with fuzzy cellular automata transition function optimized by PSO","volume":"43","author":"Uguz","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.asoc.2014.12.010","article-title":"Optimization of interval type-2 fuzzy systems for image edge detection","volume":"47","author":"Gonzalez","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s00500-014-1567-3","article-title":"Dynamic Parameter Adaptation in Particle Swarm Optimization Using Interval Type-2 Fuzzy Logic","volume":"20","author":"Olivas","year":"2016","journal-title":"Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vikhar, P.A. (2016, January 22\u201324). Evolutionary algorithms: A critical review and its future prospects. Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India.","DOI":"10.1109\/ICGTSPICC.2016.7955308"},{"key":"ref_14","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, Australia."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Weikert, D., Mai, S., and Mostaghim, S. (2020). Particle Swarm Contour Search Algorithm. Entropy, 22.","DOI":"10.3390\/e22040407"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dumitru, D., Andreica, A., Dio\u015fan, L., and Balint, Z. (2020, January 8\u201312). Evolutionary Curriculum Learning Approach for Transferable Cellular Automata Rule Optimization. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, Cancun, Mexico.","DOI":"10.1145\/3377929.3389911"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.procs.2020.09.044","article-title":"Robustness analysis of transferable cellular automata rules optimized for edge detection","volume":"176","author":"Dumitru","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"91","DOI":"10.2307\/1571915","article-title":"Textures: A Photographic Album for Artists and Designers by Phil Brodatz","volume":"1","author":"Hersey","year":"1968","journal-title":"Leonardo"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2010). Inductive Transfer. Encyclopedia of Machine Learning, Springer US.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1016\/j.jcmg.2019.06.009","article-title":"State-of-the-Art Deep Learning in Cardiovascular Image Analysis","volume":"12","author":"Litjens","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_21","unstructured":"Jain, R., Kasturi, R., and Schunck, B.G. (1995). Edge Detection, McGraw-Hill. Machine Vision."},{"key":"ref_22","first-page":"537","article-title":"Edge Detection Techniques-An Overview","volume":"8","author":"Ziou","year":"1998","journal-title":"Pattern Recognit. Image Anal. C"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Canny, J. (1986). A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 679\u2013698.","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"ref_24","unstructured":"Burks, A.W. (1966). Theory of Self-Reproducing Automata, University of Illinois Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.tcs.2004.11.021","article-title":"Theory of Cellular Automata: A Survey","volume":"334","author":"Kari","year":"2005","journal-title":"Theor. Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the Amount of Ecologic Association Between Species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"scikit-image: Image processing in Python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"},{"key":"ref_28","first-page":"68","article-title":"Analysis of Image Segmentation Methods Based on Performance Evaluation Parameters","volume":"4","author":"Xess","year":"2014","journal-title":"Int. J. Comput. Eng. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Zhou","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","first-page":"2","article-title":"An Isotropic 3x3 Image Gradient Operator","volume":"2014","author":"Sobel","year":"1968","journal-title":"Present. Stanf. A.I. Proj."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/4\/414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:25:35Z","timestamp":1760361935000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/4\/414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,31]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["e23040414"],"URL":"https:\/\/doi.org\/10.3390\/e23040414","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2021,3,31]]}}}