Yu Taoyuan, Xu Xiping, Li Yuanpeng, Zhang Ning, Zhang Naiyu, Wang Xiaohui
School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, China.
Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, Jilin, China.
Sci Rep. 2025 Jan 25;15(1):3270. doi: 10.1038/s41598-025-87970-0.
In order to address the issue of tracking errors of collision Caenorhabditis elegans, this research proposes an improved particle filter tracking method integrated with cultural algorithm. The particle filter algorithm is enhanced through the integration of the sine cosine algorithm, thereby facilitating uninterrupted tracking of the target C. elegans. Furthermore, the cultural algorithm is employed to facilitate recognition of the target C. elegans following a collision. In addition, this method integrates the concepts of down-sample and marking to reduce the average processing time of the image. Ultimately, the experiment was conducted on two strains of C. elegans of six ages. The experimental results demonstrate that the proposed method can accurately identify the target worm in the post-collision stage. The proposed method has the potential to be utilized in the field of worm tracking, offering a novel method into the acquisition of collision C. elegans behavior.
为了解决秀丽隐杆线虫碰撞跟踪误差问题，本研究提出一种结合文化算法的改进粒子滤波跟踪方法。通过融入正弦余弦算法对粒子滤波算法进行增强，从而便于对目标秀丽隐杆线虫进行不间断跟踪。此外，采用文化算法来促进碰撞后对目标秀丽隐杆线虫的识别。另外，该方法整合了下采样和标记的概念以减少图像的平均处理时间。最终，对六个年龄的两株秀丽隐杆线虫进行了实验。实验结果表明，所提方法能够在碰撞后阶段准确识别目标线虫。所提方法具有在线虫跟踪领域得到应用的潜力，为获取秀丽隐杆线虫碰撞行为提供了一种新方法。
