Lan Kun, Zhou Jianqiang, Jiang Xiaoliang, Wang Jun, Huang Shigao, Yang Jie, Song Qun, Tang Rui, Gong Xueyuan, Liu Kexing, Wu Yaoyang, Li Tengyue
College of Mechanical Engineering, Quzhou University, Quzhou, China.
Department of Radiation Oncology, First Affiliated Hospital of Air Force Medical University, Xi'an, China.
Quant Imaging Med Surg. 2023 Mar 1;13(3):1312-1322. doi: 10.21037/qims-22-295. Epub 2022 Oct 8.
Image segmentation is an important step during the processing of medical images. For example, for the computer aid diagnostic systems for lung cancer image analysis, the segmented regions of tumors would help doctors in early diagnosis to determine timely and appropriate treatment possibilities and thereby improve the survival rate of the patients. However, general clinical routines of manual segmentation for large number of medical images are very difficult and time consuming, which is the challenge we aim to tackle using our proposed method.
A novel image segmentation method with evolutionary learning technique named Group Theoretic Particle Swarm Optimization is proposed. It can tackle multi-level thresholding optimization problem during the segmentation process and rebuild the search paradigm according to the solid mathematical foundation of symmetric group from four designable aspects, which are particle encoding, solution landscape, neighborhood movement and swarm topology, respectively. The Kapur's entropy of multi-level thresholds is assessed as the objective function.
In contrast to those conventional metaheuristics methods for lung cancer image segmentation, this newly presented method generates the best performance result among them. Experimental results show that its Kapur's entropy has the value of 9.07, which is 16% higher than the worst case. Computational time is acceptable at the cost of 173.730 seconds, average level of evaluation metrics [Kappa, Precision, Recall, F1-measure, intersection over union (IoU) and receiver operating characteristic (ROC)] is over 90%, and search process of multi-level threshold combination would finally converge in the later phase of iterations after 700. The ablation study indicates that all components are significant to the contributions of our proposed method.
Group Theoretic Particle Swarm Optimization for multi-level threshold segmentation is an efficient way to split a medical image into distinct regions and extract tumor tissues regions from the background. It maintains the balanced relationship between diversification and intensification during the search process and helps clinicians to make the diagnosis more accurately. Our proposed method processes potential medical value and clinical meanings.
图像分割是医学图像处理过程中的重要步骤。例如，对于肺癌图像分析的计算机辅助诊断系统，肿瘤的分割区域有助于医生进行早期诊断，以确定及时且合适的治疗方案，从而提高患者的生存率。然而，对大量医学图像进行手动分割的常规临床流程非常困难且耗时，这正是我们旨在通过所提出的方法来解决的挑战。
提出了一种采用进化学习技术的新型图像分割方法，即群论粒子群优化算法。它能够在分割过程中解决多级阈值优化问题，并基于对称群坚实的数学基础，从粒子编码、解空间景观、邻域移动和群体拓扑这四个可设计的方面重建搜索范式。将多级阈值的卡普尔熵评估为目标函数。
与那些用于肺癌图像分割的传统元启发式方法相比，这种新提出的方法在它们之中产生了最佳性能结果。实验结果表明，其卡普尔熵值为9.07，比最差情况高16%。计算时间为173.730秒，在可接受范围内，评估指标[卡帕、精确率、召回率、F1值、交并比（IoU）和受试者工作特征曲线（ROC）]的平均水平超过90%，多级阈值组合的搜索过程最终在700次迭代的后期阶段收敛。消融研究表明，所有组件对我们所提出方法的贡献都很显著。
用于多级阈值分割的群论粒子群优化算法是将医学图像分割成不同区域并从背景中提取肿瘤组织区域的有效方法。它在搜索过程中保持了多样化和强化之间的平衡关系，并帮助临床医生更准确地进行诊断。我们所提出的方法具有潜在的医学价值和临床意义。
