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Objective: By studying the recognition effect of ultrasonic biological image data analysis on muscle group motion function, the evaluation value and significance of ultrasonic biomedical image combination algorithm on muscle group motion function are discussed.  Methods: A Gabor filtering algorithm is proposed to smooth the original image. The MVEF algorithm is used to enhance the ultrasonic image and binary further the image again. Using the principle of the Hove transform, the thickness of the muscle is automatically estimated.  Results: The square of correlation coefficients of the manual measurement method, Gabor filtering algorithm and MVEF algorithm are 91.3%, 91.3% and 87.8%, respectively. The difference between the manual measurement and the estimation based on the Gabor filtering algorithm is 1.45 ± 0.48mm. The difference between the results of manual measurement and the MVEF algorithm is 1.38 ± 0.56mm. The computation time of the MVEF algorithm and Gabor algorithm are 5 seconds and 0.3 seconds, respectively.  Conclusions: The algorithm proposed in this study can effectively measure the muscle thickness, fast, convenient and accurate, and can reflect the contractility of skeletal muscle well, which is of great value for the recognition and evaluation of muscle group movement function. Level of evidence II; Therapeutic studies - investigation of treatment results.

ANALYSIS OF ULTRASOUND IMAGE BIOLOGICAL IMAGE ALGORITHM IN THE RESTORATION OF MUSCLE GROUP MOVEMENT FUNCTION