Healthc Inform Res.  2016 Oct;22(4):299-304. 10.4258/hir.2016.22.4.299.

Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System

• Park BE
• Jang WS
• Yoo SK

• KMID: 2357384
• DOI: http://doi.org/10.4258/hir.2016.22.4.299

Abstract

OBJECTIVES
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 a t-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.

Keyword

Rotator Cuff; Ultrasonography; Support Vector Machine; Computer-Assisted Image Analysis; Statistical Data Analyses

MeSH Terms

Classification
Data Interpretation, Statistical
Entropy
Image Processing, Computer-Assisted
Rotator Cuff
Sensitivity and Specificity
Subject Headings
Support Vector Machine
Tears
Tendons
Ultrasonography*

Reference

1. Chakravarty K, Webley M. Shoulder joint movement and its relationship to disability in the elderly. J Rheumatol. 1993; 20(8):1359–1361.

2. Chen WM, Chang RF, Kuo SJ, Chang CS, Moon WK, Chen ST, et al. 3-D ultrasound texture classification using run difference matrix. Ultrasound Med Biol. 2005; 31(6):763–770.

3. Horng MH. Texture classification of the ultrasonic images of rotator cuff diseases based on radial basis function network. In : Proceedings of 2008 IEEE International Joint Conference on Neural Networks; 2008 Jun 1-8; Hong Kong. p. 91–97.

4. Wikipedia. Supraspinatus muscle [Internet]. [place unknown]: Wikipedia;c2016. cited at 2016 Oct 11. Available from: https://en.wikipedia.org/wiki/Supraspinatus_muscle.

5. Lim JY, Choi JE, Kim MJ, Kim S, Kim Y, Do HK, et al. Comparative effectiveness research of conservative treatment and rotator cuff repair for the patient with rotator cuff tears. Seoul, Korea: National Evidence-based Healthcare Collaborating Agency;2015.

6. Haralick RM, Shanmugam K. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973; 3(6):610–621.

7. Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process. 1975; 4(2):172–179.

8. Mavroforakis ME, Georgiou HV, Dimitropoulos N, Cavouras D, Theodoridis S. Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artif Intell Med. 2006; 37(2):145–162.

9. Chen YW, Lin CJ. Combining SVMs with various feature selection strategies. In : Chen YW, Lin CJ, editors. Feature extraction. Heidelberg, Germany: Springer;2006. p. 315–324.

10. Wikipedia. Sensitivity and specificity [Internet]. [place unknown]: Wikipedia;c2016. cited at 2016 Oct 11. Available from: https://en.wikipedia.org/wiki/Sensitivity_and_specificity.

11. Andarawis-Puri N, Ricchetti ET, Soslowsky LJ. Rotator cuff tendon strain correlates with tear propagation. J Biomech. 2009; 42(2):158–163.