College of Physical Education, Beihua University, Jilin, China.
Front Public Health. 2022 Mar 17;10:842452. doi: 10.3389/fpubh.2022.842452. eCollection 2022.
The Beijing 2022 Winter Olympics will begin soon, which is mainly focused on winter sports. Athletes from different countries will arrive in Beijing one after another for training and competition. The health protection of athletes of winter sports is very important in training and competition. The occurrence of sports injury is characterized by multiple factors, uncertainty, and accidents. This paper mainly pays attention to the head injury with the highest severity. Athletes' high safety awareness is a part of reducing injury, but safety awareness cannot effectively reduce the occurrence of injury in competition, and timely treatment of injured athletes is particularly important. After athletes are injured, a telemedicine image acquisition system can be built, so that medical experts can identify athletes' injuries in time and provide the basis for further diagnosis and treatment. In order to improve the accuracy of medical image processing, a C-support vector machine (SVM) medical image segmentation method combining the Chan-Vese (CV) model and SVM is proposed in this paper. After segmentation, the edge and detail features of the image are more prominent, which meet the requirements of high precision for medical image segmentation. Meanwhile, a high-precision registration algorithm of brain functional time-series images based on machine learning (ML) is proposed, and the automatic optimization of high-precision registration of brain function time-series images is performed by ML algorithm. The experimental results show that the proposed algorithm has higher segmentation accuracy above 80% and less registration time below 40 ms, which can provide a reference for doctors to quickly identify the injury and shorten the time.
北京 2022 年冬季奥运会即将开幕，主要关注冬季运动。来自不同国家的运动员将陆续抵达北京进行训练和比赛。冬季运动运动员的健康保护在训练和比赛中非常重要。运动损伤的发生具有多因素、不确定性和意外性的特点。本文主要关注最严重的头部损伤。运动员的高安全意识是减少损伤的一部分，但安全意识不能有效减少比赛中的损伤发生，及时治疗受伤运动员尤为重要。运动员受伤后，可建立远程医疗图像采集系统，以便医疗专家及时识别运动员的受伤情况，并为进一步诊断和治疗提供依据。为了提高医学图像处理的准确性，本文提出了一种结合 Chan-Vese（CV）模型和支持向量机（SVM）的 C-支持向量机（SVM）医学图像分割方法。分割后，图像的边缘和细节特征更加突出，满足了医学图像分割的高精度要求。同时，提出了一种基于机器学习（ML）的脑功能时间序列图像高精度配准算法，通过 ML 算法实现脑功能时间序列图像高精度配准的自动优化。实验结果表明，该算法的分割精度高于 80%，配准时间小于 40ms，可为医生快速识别损伤和缩短时间提供参考。
