通过正电子发射断层扫描 (PET) 和计算机断层扫描 (CT) 图像进行的自动肿瘤分割在通过放射肿瘤学预防、诊断和治疗这种疾病中发挥着关键作用。然而，由于灰度水平的异质性和模糊边界，分割这些肿瘤具有挑战性。为了解决这些问题，本文提出了一种有效的基于模型的 PET/CT 肿瘤联合分割方法，该方法结合了模糊 C 均值聚类和贝叶斯分类信息。为了缓解多模态图像的灰度异质性，该方法基于PET的背景区域信息和CT的前景区域信息，设计了一种新的灰度相似区域项。创新性地提出了边缘停止函数，通过结合模糊 C 均值聚类策略来增强模糊边缘的定位。为了进一步提高分割精度，结合PET图像中像素点的分布特征，在PET图像的基础上引入了独特的数据保真度项。最后，对头颈肿瘤 (HECKTOR) 和非小细胞肺癌 (NSCLC) 数据集的实验验证表明，DSC、RVD 和 HD5 等三个关键评估指标具有令人印象深刻的值，分别达到 0.85、5.32 和 0.17 ， 分别。这些引人注目的结果表明，基于数学模型的图像分割方法在处理多模态图像中的灰度异质性和模糊边界方面表现出出色的性能。版权所有 © 2024 Elsevier Ltd. 保留所有权利。

Automatic tumor segmentation via positron emission tomography (PET) and computed tomography (CT) images plays a critical role in the prevention, diagnosis, and treatment of this disease via radiation oncology. However, segmenting these tumors is challenging due to the heterogeneity of grayscale levels and fuzzy boundaries. To address these issues, in this paper, an efficient model-informed PET/CT tumor co-segmentation method that combines fuzzy C-means clustering and Bayesian classification information is proposed. To alleviate the grayscale heterogeneity of multi-modal images, in this method, a novel grayscale similar region term is designed based on the background region information of PET and the foreground region information of CT. An edge stop function is innovatively presented to enhance the localization of fuzzy edges by incorporating the fuzzy C-means clustering strategy. To improve the segmentation accuracy further, a unique data fidelity term is introduced based on PET images by combining the distribution characteristics of pixel points in PET images. Finally, experimental validation on datasets of head and neck tumor (HECKTOR) and non-small cell lung cancer (NSCLC) demonstrated impressive values for three key evaluation metrics, including DSC, RVD and HD5, achieved impressive values of 0.85, 5.32, and 0.17, respectively. These compelling results indicate that image segmentation methods based on mathematical models exhibit outstanding performance in handling grayscale heterogeneity and fuzzy boundaries in multi-modal images.Copyright © 2024 Elsevier Ltd. All rights reserved.