Abstract
In this paper we propose a fully automatic 2D prostate segmentation algorithm using fused ultrasound (US) and elastography images. We show that the addition of information from mechanical tissue properties acquired from elastography to acoustic information from B-mode ultrasound, can improve segmentation results. Gray level edge similarity and edge continuity in both US and elastography images deform an Active Shape Model. Comparison of automatic and manual contours on 107 transverse images of the prostate show a mean absolute error of 2.6 ±0.9 mm and a running time of 17.9 ±12.2 s. These results show that the combination of the high contrast elastography images with the more detailed but low contrast US images can lead to very promising results for developing an automatic 3D segmentation algorithm.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Penna, M.A., Dines, K.A., Seip, R., Carlson, R.F., Sanghvi, N.T.: Modeling prostate anatomy from multiple view TRUS images for image-guided HIFU therapy. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54(1), 52–69 (2007)
Tutar, I.B., Pathak, S.D., Gong, L., Cho, P.S., Wallner, K., Kim, Y.: Semiautomatic 3-D prostate segmentation from TRUS images using spherical harmonics. IEEE Trans. Med. Imaging 25(12), 1645–1654 (2006)
Zhan, Y., Shen, D.: Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method. IEEE Trans. Med. Imaging 25(3), 256–272 (2006)
Hodge, A.C., Fenster, A., Downey, D.B., Ladak, H.M.: Prostate boundary segmentation from ultrasound images using 2D active shape models: optimization and extension to 3D. Comput. Methods Programs Biomed. 84(2-3), 99–113 (2006)
Shen, D., Zhan, Y., Davatzikos, C.: Segmentation of prostate boundaries from ultrasound images using statistical shape model. IEEE Trans. Med. Imaging 22(4), 539–551 (2003)
Ophir, J., Cépedes, I., Ponnekanti, H., Yazdi, Y., Li, X.: Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason. Imaging 13(2), 111–134 (1991)
Cochlin, D.L., Ganatra, R.H., Griffiths, D.F.R.: Elastography in the detection of prostatic cancer. Clin. Radiol. 57(11), 1014–1020 (2002)
Souchon, R., Hervieu, V., Gelet, A., Ophir, J., Chapelon, J.: Human prostate elastography: in vitro study. In: IEEE Symposium on Ultrasonics, vol. 2, pp. 1251–1253 (2003)
Mahdavi, S.S., Moradi, M., Wen, X., Morris, W.J., Salcudean, S.E.: Vibro-elastography for visualization of the prostate region: method evaluation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 339–347. Springer, Heidelberg (2009)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)
Zahiri-Azar, R., Salcudean, S.E.: Motion estimation in ultrasound images using time domain cross correlation with prior estimates. IEEE Trans. Biomed. Eng. 53(10), 1990–2000 (2006)
Mahdavi, S.S., Morris, W.J., Spadinger, I., Chng, N., Goksel, O., Salcudean, S.E.: 3D prostate segmentation in ultrasound images based on tapered and deformed ellipsoids. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 960–967. Springer, Heidelberg (2009)
Abolmaesumi, P., Sirouspour, M.R.: An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images. IEEE Trans. Med. Imaging 23(6), 772–784 (2004)
von Lavante, E., Noble, J.A.: Segmentation of breast cancer masses in ultrasound using radio-frequency signal derived parameters and strain estimates. In: IEEE ISBI, pp. 536–539 (2008)
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
Mahdavi, S.S., Moradi, M., Morris, W.J., Salcudean, S.E. (2010). Automatic Prostate Segmentation Using Fused Ultrasound B-Mode and Elastography Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15745-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-15745-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15744-8
Online ISBN: 978-3-642-15745-5
eBook Packages: Computer ScienceComputer Science (R0)