Abstract
Segmentation is one of the fundamental tasks in computer vision applications. The nature of ultrasound images, which are subject to multiplicative noise instead of the widely used additive noise modeling, leads to problems of standard segmentation algorithms. In this paper we propose a new level set approach for the segmentation of medical ultrasound data. The advantage of this approach is both its simpleness and robustness: the noise inherent in ultrasound images does not have to be modeled explicitly but is rather estimated by means of discriminant analysis. In particular, we determine an optimal threshold, which enables us to separate two signal distributions in the intensity histogram and incorporate this information in the evolution of the level set contour. The superiority of our approach over the popular Chan-Vese formulation is demonstrated on real 2D patient data from echocardiography.
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
Chan, T.F., Vese, L.A.: Active Contours Without Edges. IEEE-TIP 10(2), 266–277 (2001)
Chan, T.F., Vese, L.A.: A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model. IJCV 50(3), 271–293 (2002)
Ma, M., et al.: Model Driven Quantification of Left Ventricular Function from Sparse Single-Beat 3D Echocardiography. Med. Img. Anal. 14, 582–593 (2010)
Noble, J.A., Boukerroui, D.: Ultrasound Image Segmentation: A Survey. IEEE-TMI 25(8), 987–1010 (2006)
Osher, S., Fedkiw, R.P.: Level Set Methods and Dynamic Implicit Surfaces. Springer, New York (2003)
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE-TSMC 9(1), 62–66 (1979)
Sawatzky, A., Tenbrinck, D., Jiang, X., Burger, M.: A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models. J. Math. Imaging and Vision (2013), doi:10.1007/s10851-013-0419-6
Tenbrinck, D., Sawatzky, A., Jiang, X., Burger, M., Haffner, W., Willems, P., Paul, M., Stypmann, J.: Impact of Physical Noise Modeling on Image Segmentation in Echocardiography. Eurographics Workshop on Vis. Comp. for Bio. and Med., 33–40 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tenbrinck, D., Jiang, X. (2013). Discriminant Analysis Based Level Set Segmentation for Ultrasound Imaging. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-40246-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40245-6
Online ISBN: 978-3-642-40246-3
eBook Packages: Computer ScienceComputer Science (R0)