Abstract
Orthopaedic examinations are a major reason for radiographic image acquisition. For many diagnostic problems measurements have to be computed from the recorded radiographs. As this task is time-consuming and lacks objectivity, it is desirable to perform these measurements automatically via so-called computational imaging. This requires robust and accurate methods trained and proven on clinical data.
We propose a fully automatic technique and present the three complementing stages of our segmentation algorithm. We evaluated the proposed method on more than 200 clinical images and achieve robust and precise delineations, well-suited for automated computation of orthopaedic measurements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adelson, E., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M.: Pyramid methods in image processing. RCA Engineer 29(6), 33–41 (1984)
Ballard, D.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)
Boewer, M., Arndt, H., Ostermann, P.W., Petersein, J., Mutze, S.: Length and angle measurements of the lower extremity in digital composite overview images. European Radiology 15(1), 158–164 (2005)
Chav, R., Cresson, T., Kauffmann, C., de Guise, J.A.: Method for fast and accurate segmentation processing from prior shape: Application to femoral head segmentation on X-ray images. In: Pluim, J.P.W., Dawant, B.M. (eds.) Medical Imaging, vol. 7259, p. 72594y. SPIE, San Jose (2009)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Gooßen, A., Hermann, E., Gernoth, T., Pralow, T., Grigat, R.R.: Model-based lower limb segmentation using weighted multiple candidates. In: Meinzer, H.-P., Deserno, T.M., Handels, H., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin, pp. 376–380. Springer, Heidelberg (2010)
Gooßen, A., Peters, D., Gernoth, T., Pralow, T., Grigat, R.R.: Intelligent feature selection for model-based bone segmentation in digital radiographs. In: Technology and Applications in Biomedicine, pp. 1–4. IEEE Computer Society Press, Los Alamitos (2009)
Gooßen, A., Schlüter, M., Pralow, T., Grigat, R.R.: A stitching algorithm for automatic registration of digital radiographs. In: Campilho, A., Kamel, M. (eds.) Image Analysis and Recognition, pp. 854–862. Springer, Heidelberg (2008)
Hankemeier, S., Gosling, T., Richter, M., Hufner, T., Hochhausen, C., Krettek, C.: Computer-assisted analysis of lower limb geometry: higher intraobserver reliability compared to conventional method. Computer Aided Surgery 11(2), 81–86 (2006)
Hoerr, N.L., Pyle, S.I., Francis, C.C.: Radiographic atlas of skeletal development of the foot and ankle, A standard of reference. C. C. Thomas, Springfield (1962)
Pyle, S.I., Hoerr, N.L.: Radiographic Atlas of the skeletal development of the knee, A standard of reference. Blackwell Scientific Publications, Oxford (1955)
Ruppertshofen, H., Lorenz, C., Beyerlein, P., Salah, Z., Rose, G., Schramm, H.: Fully automatic model creation for object localization utilizing the generalized hough transform. In: Meinzer, H.-P., Deserno, T.M., Handels, H., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin, pp. 331–335. Springer, Heidelberg (2010)
Schramm, H., Ecabert, O., Peters, J., Philomin, V., Weese, J.: Towards fully automatic object detection and segmentation. In: Reinhardt, J.M., Pluim, J.P.W. (eds.) Medical Imaging, vol. 6144, p. 614402. SPIE, San Jose (2006)
Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. on Information Theory 13(2), 260–269 (1967)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gooßen, A., Pralow, T., Grigat, RR. (2010). Fully Automatic Joint Segmentation for Computer-Aided Diagnosis and Planning. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-15948-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15947-3
Online ISBN: 978-3-642-15948-0
eBook Packages: Computer ScienceComputer Science (R0)