In this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism optimization (EMO). The system starts with visualizing a patient's digital communication in medicine (DICOM) abdominal data set in three views. Two‐stage active contour segmentation methods that integrate region‐based local and global techniques using the active geodesic contour technique are proposed to segment the liver. To increase the accuracy and speed of segmentation for liver images, we identify the optimal threshold of the image segmentation method based on EMO with Otsu and Kapur algorithms. EMO offers interesting search capabilities while keeping a low computational cost. The proposed system was tested using a set of five DICOM data sets. All images were of the same size and stored in JPEG format (512 × 512 pixels). Experimental results illustrate that the proposed system outperforms state‐of‐the‐art methods such as the watershed algorithm. The average sensitivity, specificity, and accuracy of the segmented liver using the active contour model were 97.05%, 99.88%, and 98.47%, respectively. Moreover, the average sensitivity, specificity, and accuracy of the segmented liver tumor results were 94.15%, 99.57%, and 96.86%, respectively.

中文翻译：

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基于多级阈值和电磁优化算法的肝脏肿瘤自动分割系统

在本文中，我们提出了一种使用磁共振成像和计算机断层扫描的肝脏肿瘤自动分割系统。所提出的系统基于具有电磁优化（EMO）的多级阈值算法。该系统首先在三个视图中可视化患者的医学数字通信 (DICOM) 腹部数据集。提出了使用主动测地线轮廓技术整合基于区域的局部和全局技术的两阶段主动轮廓分割方法来分割肝脏。为了提高肝脏图像分割的准确性和速度，我们使用 Otsu 和 Kapur 算法确定基于 EMO 的图像分割方法的最佳阈值。EMO 提供有趣的搜索功能，同时保持较低的计算成本。使用一组五个 DICOM 数据集对建议的系统进行了测试。所有图像的大小相同，并以 JPEG 格式（512 × 512 像素）存储。实验结果表明，所提出的系统优于最先进的方法，如分水岭算法。使用主动轮廓模型分割肝脏的平均敏感性、特异性和准确性分别为97.05%、99.88%和98.47%。此外，分段肝肿瘤结果的平均敏感性、特异性和准确性分别为94.15%、99.57%和96.86%。使用活动轮廓模型分割肝脏的准确率分别为97.05%、99.88%和98.47%。此外，分段肝肿瘤结果的平均敏感性、特异性和准确性分别为94.15%、99.57%和96.86%。使用活动轮廓模型分割肝脏的准确率分别为97.05%、99.88%和98.47%。此外，分段肝肿瘤结果的平均敏感性、特异性和准确性分别为94.15%、99.57%和96.86%。