Zusammenfassung
Muscle ultrasonography is a convenient technique to visualize healthy and pathological muscle tissue as it is non-invasive and image acquisition can be done in real-time. In this paper, a texture-based approach is presented to detect myositis in ultrasound images automatically. We compute different texture features like wavelet transform features and first-order grey-level intensity statistics of a relevant central image patch carrying structure and intensity information of muscle tissue. Using a combination of these information we reached an accuracy of classification of 92.20 % with our approach on a training data set of 63 clinically pre-classified data sets.
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Literatur
Pillen S, van Alfen N. Skeletal muscle ultrasound. Neurol Res. 2011;33(10):1016–24.
Wu CM, Chen YC, Hsieh KS. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging. 1992;11(2):141–52.
Huang YL, Wang KL, Chen DR. Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl. 2006;15(2):164–9.
Tsiaparas NN, Golemati S, Andreadis I, et al. Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Trans Inf Technol Biomed. 2011;15(1):130–7.
Pohle R, Fischer D, von Rohden L. Computergestützte Gewebedifferenzierung bei der Skelettmuskelsonographie. Ultraschall Med. 2000;21(6):245–52.
Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell. 1989;11(7):674–93.
Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21–7.
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König, T., Rak, M., Steffen, J., Neumann, G., von Rohden, L., Tönnies, K.D. (2013). Texture-Based Detection of Myositis in Ultrasonographies. In: Meinzer, HP., Deserno, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2013. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36480-8_16
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DOI: https://doi.org/10.1007/978-3-642-36480-8_16
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