Skip to main content

Ultrasound texture-based CAD system for detecting neuromuscular diseases

International Journal of Computer Assisted Radiology and Surgery volume 10, pages 1493–1503 (2015)Cite this article

Abstract

Purpose

Diagnosis of neuromuscular diseases in ultrasonography is a challenging task since experts are often unable to discriminate between healthy and pathological cases. A computer-aided diagnosis (CAD) system for skeletal muscle ultrasonography was developed and tested for myositis detection in ultrasound images of biceps brachii.

Methods

Several types of features were extracted from rectangular and polygonal image regions-of-interest (ROIs), including first-order statistics, wavelet-based features, and Haralick’s features. Features were chosen that are sensitive to the change in contrast and structure for pathological ultrasound images of neuromuscular diseases. The number of features was reduced by applying different sequential feature selection strategies followed by a supervised principal component analysis. For classification, two linear approaches were investigated: Fisher’s classifier and the linear support vector machine (SVM) as well as the nonlinear \(k\)-nearest neighbor approach. The CAD system was benchmarked on datasets of 18 subjects, seven of which were healthy, while 11 were affected by myositis. Three expert radiologists provided pre-classification and testing interpretations.

Results

Leave-one-out cross-validation on the training data revealed that the linear SVM was best suited for discriminating healthy and pathological muscle tissue, achieving 85/87 % accuracy, 90 % sensitivity, and 83/85 % specificity, depending on the radiologist.

Conclusion

A muscle ultrasonography CAD system was developed, allowing a classification of an ultrasound image by one-click positioning of rectangular ROIs with minimal user effort. The applicability of the system was demonstrated with the challenging example of myositis detection, showing highly accurate results that were robust to imprecise user input.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Asvestas P, Golemati S, Matsopoulos GK, Nikita KS, Nicolaides AN (2002) Fractal dimension estimation of carotid atherosclerotic plaques from B-mode ultrasound: a pilot study. Ultrasound Med Biol 28:1129–1136

    Article  PubMed  Google Scholar 

  2. Awad J, Krasinski A, Parraga G, Fenster A (2010) Texture analysis of carotid artery atherosclerosis from three-dimensional ultrasound images. Med Phys 37:1382–1391

    Article  PubMed  Google Scholar 

  3. Basset O, Sun Z, Mestas J, Gimenez G (1993) Texture analysis of ultrasonic images of the prostate by means of co-occurrence matrices. Ultrason Imaging 15:218–237

    CAS  Article  PubMed  Google Scholar 

  4. Basset O, Duboeuf F, Delhay B, Brusseau E, Cachard C, Tasu J (2008) Texture analysis of ultrasound liver images with contrast agent to characterize the fibrosis stage. In: Proceedings of IEEE ultrasonics symposium, pp 24–27

  5. Chang RF, Wu WJ, Moon W, Chen DR (2005) Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res Treat 89:179–185

    Article  PubMed  Google Scholar 

  6. Chen DR, Chang RF, Huang YL (1999) Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. Radiology 213:407–412

    CAS  Article  PubMed  Google Scholar 

  7. Chen L, Seidel G, Mertins A (2010) Multiple feature extraction for early Parkinson risk assessment based on transcranial sonography image. In: Proceedings of international conference on image processing, pp 2277–2280

  8. Christodoulou C, Pattichis C, Pantziaris M, Nicolaides A (2003) Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging 22:902–912

    CAS  Article  PubMed  Google Scholar 

  9. Daubechies I (1992) Ten lectures on wavelets. Society for Industrial and Applied Mathematics

  10. Duda R, Hart P, Stork D (2001) Pattern classification. Wiley, London

    Google Scholar 

  11. Duin R, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax D, Verzakov S (2013) A MATLAB toolbox for pattern recognition. Delft Univ. Techn., http://prtools.org/, pRTools 4.2.4

  12. Finette S, Bleier A, Swindell W (1983) Breast tissue classification using diagnostic ultrasound and pattern recognition techniques: I. Methods of pattern recognition. Ultrason Imaging 5:55–70

    CAS  Article  PubMed  Google Scholar 

  13. Good MS, Rose JL, Goldberg BB (1982) Application of pattern recognition techniques to breast cancer detection: Ultrasonic analysis of 100 pathologically confirmed tissue areas. Ultrason Imaging 4:378–396

    CAS  Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  15. Huang Y, Han X, Tian X, Zhao Z, Zhao J, Hao D (2010) Texture analysis of ultrasonic liver images based on spatial domain methods. In: Proceedings of international congress on image signal processing, vol 2, pp 562–565

  16. Huang YL, Wang KL, Chen DR (2006) Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15:164–169

    Article  Google Scholar 

  17. Kier C, Seidel G, Brüggemann N, Hagenah J, Klein C, Aach T, Mertins A (2009) Transcranial sonography as early indicator for genetic Parkinson’s disease. In: European conference of the international federation for medical and biological engineering, vol 22. Springer, Berlin, pp 456–459

  18. König T, Rak M, Steffen J, Neumann G, von Rohden L, Tönnies KD (2013) Texture-based detection of myositis in ultrasonographies. Proceedings of Bildverarbeitung für die Medizin (BVM), Informatik aktuell, pp 81–86

  19. Layer G, Zuna I, Lorenz A, Zerban H, Haberkorn U, Bannasch P, van Kaick G, Räth U (1990) Computerized ultrasound B-scan texture analysis of experimental fatty liver disease: influence of total lipid content and fat deposit distribution. Ultrason Imaging 12:171–188

    CAS  Article  PubMed  Google Scholar 

  20. Lee WL, Chen YC, Hsieh KS (2003) Ultrasonic liver tissues classification by fractal feature vector based on m-band wavelet transform. IEEE Trans Med Imaging 22:382–392

    Article  PubMed  Google Scholar 

  21. Liao YY, Tsui PH, Li CH, Chang KJ, Kuo WH, Chang CC, Yeh CK (2011) Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images. Med Phys 38:2198–2207

    Article  PubMed  Google Scholar 

  22. Lu NH, Kuo CM, Ding HJ (2013) Automatic ROI segmentation in B-mode ultrasound image for liver fibrosis classification. In: Proceedings of international symposium on biometrics and security technologies (ISBAST), pp 10–13

  23. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693

    Article  Google Scholar 

  24. Minhas FU, Sabih D, Hussain M (2012) Automated classification of liver disorders using ultrasound images. J Med Sys 36:3163–3172

    Article  Google Scholar 

  25. Mohamed S, Salama M (2008) Prostate cancer spectral multifeature analysis using TRUS images. IEEE Trans Med Imaging 27:548–556

    CAS  Article  PubMed  Google Scholar 

  26. Ogawa K, Fukushima M, Kubota K, Hisa N (1998) Computer-aided diagnostic system for diffuse liver diseases with ultrasonography by neural networks. IEEE Trans Nucl Sci 45:3069–3074

    Article  Google Scholar 

  27. Pillen S, van Alfen N (2010) Skeletal muscle ultrasound. Eur J Transl Myol 1:145–155

    Article  Google Scholar 

  28. Pohle R, Fischer D, von Rohden L (2000) Computer-supported tissue characterization in ultrasound images of neuromuscular diseases. Ultrasound Med 21:245–252

    CAS  Google Scholar 

  29. Reimers K, Reimers CD, Wagner S, Paetzke I, Pongratz DE (1993) Skeletal muscle sonography: a correlative study of echogenicity and morphology. J Med Ultrasound 12:73–77

    CAS  Google Scholar 

  30. von Rohden L, Wien F, Ptzsch S (2007) Myosonographie neuromuskulärer Erkrankungen unter besonderer Berücksichtigung des Kindes- und Jugendalters. Klin Neurophysiol 38:141–154

    Article  Google Scholar 

  31. Sakalauskas A, Lukoševičius A, Laučkaite K (2011) Texture analysis of transcranial sonographic images for Parkinson disease diagnostics. Ultragarsas 66:32–36

    Google Scholar 

  32. Sakr AA, Fares ME, Ramadan M (2014) Automated focal liver lesion staging classification based on Haralick texture features and multi-SVM. Int J Comput Appl 91(8):17–25

  33. Sheppard M, Shih L (2007) P6C-2 image texture clustering for prostate ultrasound diagnosis. In: Proceedings of IEEE ultrasonic symposium, pp 2473–2476

  34. Su Y, Wang Y, Jiao J, Guo Y (2011) Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features. Open Med Inf J 5:26–37

    Article  Google Scholar 

  35. Tsiaparas NN, Golemati S, Andreadis I, Stoitsis JS, Valavanis I, Nikita KS (2011) Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Trans Inf Technol Biomed 15:130–137

    Article  PubMed  Google Scholar 

  36. Webb AR (2002) Statistical pattern recognition. Wiley, New York

    Book  Google Scholar 

  37. Wilhjelm J, Gronholdt ML, Wiebe B, Jespersen S, Hansen L, Sillesen H (1998) Quantitative analysis of ultrasound B-mode images of carotid atherosclerotic plaque: correlation with visual classification and histological examination. IEEE Trans Med Imaging 17:910–922

    CAS  Article  PubMed  Google Scholar 

  38. Wu CM, Chen YC, Hsieh KS (1992) Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging 11:141–152

    CAS  Article  PubMed  Google Scholar 

  39. Wu WJ, Lin SW, Moon WK (2012) Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Comput Med Imaging Graph 36(8):627–633

    Article  PubMed  Google Scholar 

  40. Zhou S, Shi J, Zhu J, Cai Y, Wang R (2013) Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image. Biomed Signal Process Control 8(6):688–696

    Article  Google Scholar 

Download references

Conflict of interest

T. König, M. Rak, J. Steffen, G. Neumann, L. von Rohden, and K. D. Tonnies hereby declare that they have no conflict of interest with any financial organization regarding the material discussed in the manuscript.

Ethical standards

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from all patients for being included in the study.

Author information

Affiliations

  1. Department of Simulation and Graphics, Otto von Guericke University, Universitätsplatz 2, 39106, Magdeburg, Germany

    Tim König, Johannes Steffen, Marko Rak & Klaus D. Tönnies

  2. Department of Radiology and Nuclear Medicine, Otto von Guericke University, Leipziger Straße 44, 39120, Magdeburg, Germany

    Grit Neumann & Ludwig von Rohden

Authors
  1. Tim König
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johannes Steffen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marko Rak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Grit Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ludwig von Rohden
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Klaus D. Tönnies
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tim König.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

König, T., Steffen, J., Rak, M. et al. Ultrasound texture-based CAD system for detecting neuromuscular diseases. Int J CARS 10, 1493–1503 (2015). https://doi.org/10.1007/s11548-014-1133-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-014-1133-6

Keywords

  • Texture analysis
  • Computer-aided diagnosis
  • Ultrasound imaging
  • Neuromuscular diseases
  • Classification