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.

使用计算机断层扫描（CT）分割肝脏和肝脏病变是医生准确识别肝脏异常并降低肝脏手术风险的关键和挑战性任务。这项研究提出了一种新颖的动态方法来改进模糊c均值（FCM）聚类算法，以从CT扫描中自动定位和分割肝脏和肝脏病变。更具体地说，我们基于快速FCM，混沌理论和生物启发式蚁狮优化器（ALO）（名为（CALOFCM））针对自动肝和肝病变，开发了一种在准确性，速度和最佳收敛性方面的强大优化方法。分割。我们采用ALO指导FCM确定分割过程的最佳聚类质心。我们使用混沌理论从收敛速度和避免局部最小值方面提高了ALO的性能。此外，基于混沌理论的ALO可以防止FCM陷入局部极小值并提高计算性能，从而提高稳定性，降低迭代过程的灵敏度，并允许FCM使用最佳质心。我们在一组具有腹部肝脏CT图像的患者上验证了该方法的有效性，与其他流行技术相比，结果显示出良好的检测和分割性能。这种新的混合方法可以更早，更系统地对肝病变进行临床诊断，从而帮助医学专家做出决策。基于混沌理论的ALO防止了FCM陷入局部极小值并提高了计算性能，从而提高了稳定性，降低了迭代过程的敏感性，并允许FCM使用最佳质心。我们在一组具有腹部肝脏CT图像的患者上验证了该方法的有效性，与其他流行技术相比，结果显示出良好的检测和分割性能。这种新的混合方法可以更早，更系统地对肝病变进行临床诊断，从而帮助医学专家做出决策。基于混沌理论的ALO防止了FCM陷入局部最小值并提高了计算性能，从而提高了稳定性，降低了迭代过程的灵敏度，并允许FCM使用最佳质心。我们对一组具有腹部肝脏CT图像的患者验证了该方法的有效性，与其他流行技术相比，结果显示出良好的检测和分割性能。这种新的混合方法可以更早，更系统地对肝病变进行临床诊断，从而帮助医学专家做出决策。与其他流行技术相比，结果显示出良好的检测和分割性能。这种新的混合方法可以更早，更系统地对肝病变进行临床诊断，从而帮助医学专家做出决策。与其他流行技术相比，结果显示出良好的检测和分割性能。这种新的混合方法可以更早，更系统地对肝病变进行临床诊断，从而帮助医学专家做出决策。