Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans Subject The topic of the resource CALOFCM; Chaos theory; Computed tomography (CT); Fuzzy C-Means (FCM); Hepatic lesion; Swarm intelligence (SI) Description An account of the resource Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making. 2020 Elsevier B.V. Creator An entity primarily responsible for making the resource Anter A.M.; Bhattacharyya S.; Zhang Z. Source A related resource from which the described resource is derived Applied Soft Computing Journal, Vol-96 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.asoc.2020.106677" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asoc.2020.106677</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090040754&amp;doi=10.1016%2Fj.asoc.2020.106677&amp;partnerID=40&amp;md5=0b43d754e84d67818adac14ac91c6a33" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090040754&amp;doi=10.1016%2fj.asoc.2020.106677&amp;partnerID=40&amp;md5=0b43d754e84d67818adac14ac91c6a33</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15684946 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Anter A.M., School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Benisuef, 62511, Egypt; Bhattacharyya S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Zhang Z., School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China, Peng Cheng Laboratory, Shenzhen, 518055, China