A Proactive Technique for Pennation Angle Estimation of Skeletal Muscles Using Ultrasound Imaging
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Abstract
Purpose: The concrete construction of the musculoskeletal modeling is efficiently performed using information obtained from patients rather than collected from cadavers. In this study, we have endeavored to propose an automated technique that calculates the skeletal muscle pennation angle of patient ultrasound images and compares it with manual evaluations of the same images.
Materials and Methods: The proposed technique consists of three steps after the process of collecting the data from 30 volunteers of different muscles of the upper and lower limb. The first step is to improve the contrast in the image and identify the important details in the image through the use of two methods that depend on a fuzzy inference system, and this step is considered essential to prepare the image in the next step. The Hough Transform was used to follow the muscle fibers and draw them as lines, this is the second step. The third step is to calculate the angle and compare it with the manual evaluation that was done depending on the ultrasound machine options.
Results: The results reveal that there is a slightly difference between manual and automated evaluations of pennation angle for biceps (upper limb muscle) and gastrocnemius (lower limb muscle) as 8.6% and 0.45% respectively. Furthermore, the manual assignment of pennation angles is significantly slower, taking minutes, while the automated approach takes only a few seconds. Automated measurements take 85% more time compared to manual measurements.
Conclusion: There is no significant difference between measurements based on t-test. In future work, we aspire to a wider application of this technique to other muscles in the body and to activate it as an option available in the ultrasound device.
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| Pennation Angle Skeletal Muscle Ultrasound Imaging Fuzzy Inference System and Hough Transform Biomedical Image Applications | ||
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