Supraspinatus Segmentation from Shoulder Ultrasound Images Using a Multilayer Self-shrinking Snake

You Wei Wang, Chung Chien Lee, Chung Ming Lo

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
JournalIEEE Access
DOIs
Publication statusAccepted/In press - Jan 1 2018

Keywords

  • Elasticity
  • Image segmentation
  • Lesions
  • Nonhomogeneous media
  • Pain
  • rotator cuff
  • segmentation
  • Shoulder
  • shoulder pain
  • snake
  • Ultrasonic imaging
  • ultrasound

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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