Abstract
We present a shape prior based graph cut method which does not require user initialisation. The shape prior is generalised from multiple training shapes, rather than using singular templates as priors. Weighted directed graph construction is used to impose geometrical and smooth constraints learned from priors. The proposed cost function is built upon combining selective feature extractors. A SVM classifier is used to determine an optimal combination of features in presence of calcification, fibrotic tissues, soft plaques, and metallic stent, each of which has its own characteristics in ultrasound images. Comparative analysis on manually labelled ground-truth shows superior performance of the proposed method compared to conventional graph cut methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sonka, M., et al.: Segmentation of intravascular ultrasound images: A knowledge-based approach. T-MI 14, 719–732 (1995)
Takagi, A., et al.: Automated contour detection for high frequency intravascular ultrasound imaging: A technique with blood noise reduction for edge enhancement. Ultrasound in Medicine and Biology 26(6), 1033–1041 (2000)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. T-PAMI 26(9), 1124–1137 (2004)
Wahle, A., et al.: Plaque development, vessel curvature, and wall shear stress in coronary arteries assessed by x-ray angiography and intravascular ultrasound. MIA 10(1), 615–631 (2006)
Freedman, D., Zhang, T.: Interactive graph cut based segmentation with shape priors. In: CVPR, pp. 755–762 (2005)
Malcolm, J., Rathi, Y., Tannenbaum, A.: Graph cut segmentation with nonlinear shape priors. In: ICIP, pp. 365–368 (2007)
Vu, N., Manjunath, B.S.: Shape prior segmentation of multiple objects with graph cuts. In: CVPR, pp. 1–8 (2008)
Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal surface segmentation in volumetric images-a graph-theoretic approach. T-PAMI 28(1), 119–134 (2006)
Mulet-Parada, M., Noble, J.: 2D + T acoustic boundary detection in echocardiography. MIA 4(1), 21–30 (2000)
Filho, E., et al.: Detection & quantification of calcifications in ivus by automatic thresholding. Ultrasound in Medicine and Biology 34(1), 160–165 (2008)
Unal, G., et al.: Shape-driven segmentation of the arterial wall in intravascular ultrasound images. IEEE Trans. Info. Tech. Biomed. 12(3), 335–347 (2008)
Chan, T., Zhu, W.: Level set based shape prior segmentation. In: CVPR (2005)
Boykov, Y., Funka-Lea, G.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. IJCV 70(2), 109–131 (2006)
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
Essa, E., Xie, X., Sazonov, I., Nithiarasu, P., Smith, D. (2013). Shape Prior Model for Media-Adventitia Border Segmentation in IVUS Using Graph Cut. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2012. Lecture Notes in Computer Science, vol 7766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36620-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-36620-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36619-2
Online ISBN: 978-3-642-36620-8
eBook Packages: Computer ScienceComputer Science (R0)