title: Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System.
abstract: OBJECTIVE
  In this paper, we proposed an algorithm for recognizing a rotator cuff supraspinatus tendon tear using a texture analysis based on a histogram, gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM).
  
  
  METHODS
  First, we applied a total of 57 features (5 first order descriptors, 40 GLCM features, and 12 GLRLM features) to each rotator cuff region of interest. Our results show that first order statistics (mean, skewness, entropy, energy, smoothness), GLCM (correlation, contrast, energy, entropy, difference entropy, homogeneity, maximum probability, sum average, sum entropy), and GLRLM features are helpful to distinguish a normal supraspinatus tendon and an abnormal supraspinatus tendon. The statistical significance of these features is verified using at-test. The support vector machine classification showed accuracy using feature combinations. Support Vector Machine offers good performance with a small amount of training data. Sensitivity, specificity, and accuracy are used to evaluate performance of a classification test.
  
  
  RESULTS
  From the results, first order statics features and GLCM and GLRLM features afford 95%, 85%, and 100% accuracy, respectively. First order statistics and GLCM and GLRLM features in combination provided 100% accuracy. Combinations that include GLRLM features had high accuracy. GLRLM features were confirmed as highly accurate features for classified normal and abnormal.
  
  
  CONCLUSIONS
  This algorithm will be helpful to diagnose supraspinatus tendon tear on ultrasound images.
authors.affiliation: Grauduate School of Biomedical Engineering, Yonsei University, Seoul, Korea.
authors.forename: Byung Eun
authors.initials: BE
authors.lastname: Park
authors.affiliation: Graduate Program in Biomedical Engineering, Yonsei University and Clinical Trials Center for Medical Devices, Yonsei University Health System, Seoul, Korea.
authors.forename: Won Seuk
authors.initials: WS
authors.lastname: Jang
authors.affiliation: Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Sun Kook
authors.initials: SK
authors.lastname: Yoo
keywords: Computer-Assisted Image Analysis; Rotator Cuff; Statistical Data Analyses; Support Vector Machine; Ultrasonography