Objective: Over people’s lifetimes, the prevalence of shoulder pain exceeds 70%. In particular, 70% of shoulder pain is caused by rotator cuff lesions which are located in the supraspinatus area. The automatic and quantitative segmentation of the supraspinatus area can provide a more-objective and accurate assessment of rotator cuff lesions. Methods: In this study, 108 shoulder ultrasound images comprised the image database to evaluate the proposed segmentation method, and a multilayer selfshrinking snake (S3), based on a multilayer segmentation framework, was used to achieve optimal segmentation. Using a rough initial contour that enclosed the supraspinatus area, the modified snake was shrunken with an iteration procedure according to boundary conditions that included the elasticity, curvature, gradient, and distance. Results: In the performance evaluation, the S3 achieved an F-measure of 0.85. Conclusions: The success of the S3 could provide more-objective location information to physicians diagnosing rotator cuff lesions.
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)
Wang, Y. W., Lee, C. C., & Lo, C. M. (認可的出版社/出版中). Supraspinatus Segmentation from Shoulder Ultrasound Images Using a Multilayer Self-shrinking Snake. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2885709