{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:24:13Z","timestamp":1776227053866,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Image segmentation is a fundamental challenge in computer vision, transforming complex image representations into meaningful, analyzable components. While entropy-based multilevel thresholding techniques, including Otsu, Shannon, fuzzy, Tsallis, Renyi, and Kapur approaches, have shown potential in image segmentation, they encounter significant limitations when processing thermal images, such as poor spatial resolution, low contrast, lack of color and texture information, and susceptibility to noise and background clutter. This paper introduces a novel adaptive unsupervised entropy algorithm (A-Entropy) to enhance multilevel thresholding for thermal image segmentation. Our key contributions include (i) an image-dependent thermal enhancement technique specifically designed for thermal images to improve visibility and contrast in regions of interest, (ii) a so-called A-Entropy concept for unsupervised thermal image thresholding, and (iii) a comprehensive evaluation using the Benchmarking IR Dataset for Surveillance with Aerial Intelligence (BIRDSAI). Experimental results demonstrate the superiority of our proposal compared to other state-of-the-art methods on the BIRDSAI dataset, which comprises both real and synthetic thermal images with substantial variations in scale, contrast, background clutter, and noise. Comparative analysis indicates improved segmentation accuracy and robustness compared to traditional entropy-based methods. The framework\u2019s versatility suggests promising applications in brain tumor detection, optical character recognition, thermal energy leakage detection, and face recognition.<\/jats:p>","DOI":"10.3390\/e27050526","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T10:27:41Z","timestamp":1747218461000},"page":"526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Entropy-Based Approach for Thermal Image Segmentation Using Multilevel Thresholding"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7982-5202","authenticated-orcid":false,"given":"Thaweesak","family":"Trongtirakul","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Industrial Education, Rajamangala University of Technology Phra Nakhon, Bangkok 10300, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9435-4536","authenticated-orcid":false,"given":"Karen","family":"Panetta","sequence":"additional","affiliation":[{"name":"School of Engineering, Tufts University, Medford, MA 02155, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6683-0064","authenticated-orcid":false,"given":"Artyom M.","family":"Grigoryan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4601-4507","authenticated-orcid":false,"given":"Sos S.","family":"Agaian","sequence":"additional","affiliation":[{"name":"College of Staten Island and the Graduate Center, City University of New York (CUNY), Staten Island, NY 10314, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_2","unstructured":"Bhuvana, J., Gautam, C.K., and Bishnoi, A.K. (2024, January 29\u201330). Entropy-Based Analysis of Data Compression Techniques for Information Efficiency. Proceedings of the 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC), Debre Tabor, Ethiopia."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cover, T.M., and Thomas, J.A. (2006). Elements of Information Theory, Wiley.","DOI":"10.1002\/047174882X"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ayunts, H., Grigoryan, A., and Agaian, S. (2024). Novel Entropy for Enhanced Thermal Imaging and Uncertainty Quantification. Entropy, 26.","DOI":"10.3390\/e26050374"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109769","DOI":"10.1016\/j.sigpro.2024.109769","article-title":"A New Method For Judging Thermal Image Quality with Applications","volume":"229","author":"Agaian","year":"2025","journal-title":"Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Benbelkacem, S., Oulefki, A., Agaian, S., Zenati-Henda, N., Trongtirakul, T., Aouam, D., Masmoudi, M., and Zemmouri, M. (2022). COVI3D: Automatic COVID-19 CT image-based classification and visualization platform utilizing virtual and augmented reality technologies. Diagnostics, 12.","DOI":"10.3390\/diagnostics12030649"},{"key":"ref_7","first-page":"117340I","article-title":"Lung infection region quantification, recognition, and virtual reality rendering of CT scan of COVID-19","volume":"11734","author":"Benbelkacem","year":"2021","journal-title":"Proc. SPIE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107747","DOI":"10.1016\/j.patcog.2020.107747","article-title":"Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images","volume":"114","author":"Oulefki","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"La\u0161tovi\u010dka-Medin, G., and Karad\u017ei\u0107, D. (2023, January 6\u201310). Investigating the Efficacy of Thermal Imaging as a Tool to Detect Stress in Domestic Animals. Proceedings of the 2023 12th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro.","DOI":"10.1109\/MECO58584.2023.10154988"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5122","DOI":"10.1109\/TITS.2023.3332350","article-title":"A RGB-Thermal Image Segmentation Method Based on Parameter Sharing and Attention Fusion for Safe Autonomous Driving","volume":"25","author":"Li","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"24970","DOI":"10.1109\/JSEN.2023.3311872","article-title":"Infrared and Visible Image Fusion Using Threshold Segmentation and Weight Optimization","volume":"23","author":"Zhu","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"187572","DOI":"10.1109\/ACCESS.2024.3514941","article-title":"Unmanned Aerial Surveillance and Tracking System in Forest Areas for Poachers and Wildlife","volume":"12","author":"Sangeetha","year":"2024","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kabir, R.H., and Lee, K. (2021). Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory. Appl. Sci., 11.","DOI":"10.20944\/preprints202103.0525.v1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103277","DOI":"10.1016\/j.rineng.2024.103277","article-title":"AI-Powered IoT and UAV Systems for Real-Time Detection and Prevention of Illegal Logging","volume":"24","author":"Ramadan","year":"2024","journal-title":"Results Eng."},{"key":"ref_15","first-page":"124","article-title":"Transmission map optimization for single image dehazing","volume":"12100","author":"Trongtirakul","year":"2022","journal-title":"Multimodal Image Exploit. Learn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"143936","DOI":"10.1109\/ACCESS.2023.3344534","article-title":"Adaptive single low-light image enhancement by fractional stretching in logarithmic domain","volume":"11","author":"Trongtirakul","year":"2023","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chang, B., Hwang, B., Lim, W., Kim, H., Kang, W., Park, Y.S., and Ko, D.W. (2025). Enhancing Wildlife Detection Using Thermal Imaging Drones: Designing the Flight Path. Drones, 9.","DOI":"10.3390\/drones9010052"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ram\u00edrez-Ayala, O., Gonz\u00e1lez-Hern\u00e1ndez, I., Salazar, S., Flores, J., and Lozano, R. (2023). Real-Time Person Detection in Wooded Areas Using Thermal Images from an Aerial Perspective. Sensors, 23.","DOI":"10.3390\/s23229216"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hou, F., Zhang, Y., Zhou, Y., Zhang, M., Lv, B., and Wu, J. (2022). Review on Infrared Imaging Technology. Sustainability, 14.","DOI":"10.3390\/su141811161"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kumar, S., Malik, S., and Sumathi, P. (2022, January 24\u201326). Deep Learning-Based Border Surveillance System Using Thermal Imaging. Proceedings of the 2022 IEEE 19th India Council International Conference (INDICON), Kochi, India.","DOI":"10.1109\/INDICON56171.2022.10040010"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Doull, K.E., Chalmers, C., Fergus, P., Longmore, S., Piel, A.K., and Wich, S.A. (2021). An Evaluation of the Factors Affecting \u2018Poacher\u2019 Detection with Drones and the Efficacy of Machine-Learning for Detection. Sensors, 21.","DOI":"10.3390\/s21124074"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Davies, R., Wright, J., Ablett, S., and Maskell, S. (2025). Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data. J. Mar. Sci. Eng., 13.","DOI":"10.3390\/jmse13030457"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"613","DOI":"10.3390\/e12030613","article-title":"The Maximum Entropy Production Principle: Its Theoretical Foundations and Applications to the Earth System","volume":"12","author":"Dyke","year":"2010","journal-title":"Entropy"},{"key":"ref_24","unstructured":"Parunak, H.V.D., and Brueckner, S. (June, January 28). Entropy and Self-Organization in Multi-Agent Systems. Proceedings of the Fifth International Conference on Autonomous Agents, Montreal, QC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/TCE.2013.6626251","article-title":"No Reference Color Image Contrast and Quality Measures","volume":"59","author":"Panetta","year":"2013","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_26","unstructured":"Agaian, S.S., Lentz, K.P., and Grigoryan, A.M. (2000, January 19\u201322). A New Measure of Image Enhancement. Proceedings of the IASTED International Conference on Signal Processing & Communication, Malaga, Spain."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/TIP.2006.888338","article-title":"Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy","volume":"16","author":"Agaian","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1109\/TCE.2023.3325744","article-title":"No-Reference Quality Metrics for Image Decolorization","volume":"69","author":"Ayunts","year":"2023","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","article-title":"A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram","volume":"29","author":"Kapur","year":"1985","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/BF01016429","article-title":"Possible Generalization of Boltzmann-Gibbs Statistics","volume":"52","author":"Tsallis","year":"1988","journal-title":"J. Stat. Phys."},{"key":"ref_31","first-page":"547","article-title":"On Measures of Entropy and Information","volume":"4","year":"1961","journal-title":"Berkeley Symp. Math. Statist. Prob."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.physleta.2005.01.094","article-title":"A Step Beyond Tsallis and R\u00e9nyi Entropies","volume":"338","author":"Masi","year":"2005","journal-title":"Phys. Lett. A"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Alomani, G., and Kayid, M. (2023). Further Properties of Tsallis Entropy and Its Application. Entropy, 25.","DOI":"10.3390\/e25020199"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Fuentes, J., and Gon\u00e7alves, J. (2022). R\u00e9nyi Entropy in Statistical Mechanics. Entropy, 24.","DOI":"10.3390\/e24081080"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sen, H., and Agarwal, A. (2017, January 20\u201322). A Comparative Analysis of Entropy Based Segmentation with Otsu Method for Gray and Color Images. Proceedings of the International Conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.","DOI":"10.1109\/ICECA.2017.8203655"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wu, B., Zhu, L., Cao, J., and Wang, J. (2021). A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation. Entropy, 23.","DOI":"10.3390\/e23121599"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6389","DOI":"10.1007\/s10462-022-10157-w","article-title":"A New Fusion of Whale Optimizer Algorithm with Kapur\u2019s Entropy for Multi-Threshold Image Segmentation: Analysis and Validations","volume":"55","author":"Mohamed","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kiani, H., Safabakhsh, R., and Khadangi, E. (2009, January 17\u201318). Fast Recursive Segmentation Algorithm Based on Kapur\u2019s Entropy. Proceedings of the 2nd International Conference on Computer, Control and Communication, Karachi, Pakistan.","DOI":"10.1109\/IC4.2009.4909269"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7110","DOI":"10.3934\/mbe.2021353","article-title":"Kapur\u2019s Entropy for Multilevel Thresholding Image Segmentation Based on Moth-Flame Optimization","volume":"18","author":"Wenqi","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bondi, E., Jain, R., Aggrawal, P., Anand, S., Hannaford, R., Kapoor, A., Piavis, J., Shah, S., Joppa, L., and Dilkina, B. (2020, January 1\u20135). BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093284"},{"key":"ref_41","first-page":"2835","article-title":"A Vision From a Physical Point of View and the Information Theory on the Image Segmentation","volume":"37","author":"Torres","year":"2019","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, K., He, M., Dong, L., and Ou, C. (2024). The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation. Entropy, 26.","DOI":"10.20944\/preprints202408.0511.v1"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nomura, R., and Yagi, H. (2024). Optimum Achievable Rates in Two Random Number Generation Problems with f-Divergences Using Smooth R\u00e9nyi Entropy. Entropy, 26.","DOI":"10.3390\/e26090766"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jia, H., Peng, X., Song, W., Oliva, D., Lang, C., and Li, Y. (2019). Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based L\u00e9vy Multiverse Optimization Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11080942"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"183279","DOI":"10.1109\/ACCESS.2024.3508796","article-title":"Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"116714","DOI":"10.1016\/j.image.2022.116714","article-title":"Unsupervised and optimized thermal image quality enhancement and visual surveillance applications","volume":"105","author":"Trongtirakul","year":"2022","journal-title":"Signal Process. Image Commun."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yan, H., Liu, R., Gao, Q., Wu, Z., Chen, X., and Meng, Q. (2024, January 20\u201322). Infrared Image Segmentation Method Based on Tsallis Entropy. Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT), Yichang, China.","DOI":"10.1109\/AICIT62434.2024.10730601"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Salas-Robles, J.E., Biot-Monterde, V., and Antonino-Daviu, J.A. (2024). Current and Stray Flux Combined Analysis for Sparking Detection in DC Motors\/Generators Using Shannon Entropy. Entropy, 26.","DOI":"10.3390\/e26090744"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ulhaq, A., Adams, P., Cox, T.E., Khan, A., Low, T., and Paul, M. (2021). Automated Detection of Animals in Low-Resolution Airborne Thermal Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13163276"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"M\u00fcller, D., Soto-Rey, I., and Kramer, F. (2022). Towards a guideline for evaluation metrics in medical image segmentation. BMC Res. Notes, 15.","DOI":"10.1186\/s13104-022-06096-y"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/5\/526\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:32:44Z","timestamp":1760031164000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/5\/526"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":50,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["e27050526"],"URL":"https:\/\/doi.org\/10.3390\/e27050526","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1007\/s10791-025-09719-7","asserted-by":"object"}]},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,14]]}}}