Classification of young healthy individuals with different exercise levels based on multiple musculoskeletal ultrasound images

• Published in: Biomedical signal processing and control 2020-09, Vol.62, p.102093, Article 102093
• Main Authors: Sun, Shiyu, Xue, Wufeng, Zhou, Yongjin
• Format: Article
• Language: English
• Subjects: Exercise levels; Machine learning; Musculoskeletal ultrasound image; Texture analysis
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2020.102093

•A multiple-image-feature-selection (MIFS) framework that combines information from multiple images is proposed to classify 107 subjects (54 regular exercisers and 53 irregular exercisers) with different exercise levels.•An optimal feature set is concluded consisting of 20 features and the optimal classification accuracy exceeds the existing single-image-based method, up to 78.9 %.•By simply combining the different information of multiple skeletal muscle ultrasound images, complicated tasks related to muscle exercise in reality can be solved. Whether in sports or exercise rehabilitation, muscles are usually strengthened through exercise. In this process, the biological structure of the muscle is also changed subtly. However, it has not reported whether these changes can be reflected in ultrasonic images. Furthermore, it is a challenging and novel task to capture this nuance, and existing methods that use only one image are difficult to resolve it. Therefore, this study proposes a multiple images feature selection (MIFS) framework that combines information from multiple images, aiming to find an optimal feature set that can effectively distinguish between different exercise levels. In this study, the optimal feature set was obtained from seven differently operated musculoskeletal ultrasound images (MUI) of 107 healthy subjects (55 males, 52 females, age 21.0 ± 1.9 years) included 54 regular exercisers and 53 irregular exercisers by the MIFS framework. All ultrasound images are acquired in B-mode with an 8.5 MHz linear array ultrasound transducer. Finally, we obtain an optimal feature set consisting of 20 features, and the optimal classification accuracy exceeds the existing single-image-based method, up to 78.9 %. This preliminary study suggests nuance in muscle structure caused by different exercise levels can be captured by ultrasound images. What's more, MIFS can more accurately deal with complicated task of classifying between regular and irregular exercisers as well.