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Ultrasound texture-based CAD system for detecting neuromuscular diseases

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

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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.

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Authors and 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
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  2. Johannes Steffen
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  3. Marko Rak
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  5. Ludwig von Rohden
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  6. Klaus D. Tönnies
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Corresponding author

Correspondence to Tim König.

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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

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

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