Muscles have various forms; therefore, their deformation patterns vary during contraction, which combine rotational and translational movements. Pennation angle assessment alone cannot accurately represent muscle movements. This study aimed to develop an image analysis algorithm that automatically tracks muscle displacement and to test its accuracy in fusiform and pennate muscles. In this study, twelve participants performed voluntary knee flexion–extension at 25°, 50°, and 75°. The semitendinosus and vastus lateralis muscles were imaged using ultrasonography to assess the fusiform and pennate muscles, respectively. From the acquired images, manually selected region of interest was divided into three layers (superficial, middle, and deep), and the displacement was analyzed using the proposed image analysis algorithm. This algorithm extracts multiple feature points and tracks automatically based on the Kanade–Lucas–Tomasi method, and calculates the displacement of each muscle layer from multiple points. The results presented the displacement of fusiform and pennate muscles, showing translational and rotational displacements of the three layers in the former and latter, respectively. Tracked muscle displacements strongly correlated with angular changes in the knee joints (r > 0.98) and closely matched manual analysis (r = 0.82–0.95). Accuracy decreased with increasing displacement ranges and was lower for fusiform than pennate muscles (maximum error: fusiform: 1.48, 2.15, and 2.87 mm; pennate: 1.67, 1.44, and 1.91 mm, at 25°, 50°, and 75°, respectively). The proposed method has potential for application in both clinical and basic research.
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