Abstract
The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers.
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.
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Cardinal, M.H.R., Meunier, J., Soulez, G., Maurice, R.L., Therasse, E., Cloutier, G.: Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions. TMI 25(5), 590–601 (2006)
Brusseau, E., de Korte, C.L., Mastik, F., Schaar, J., van der Steen, A.F.W.: Fully automatic luminal contour segmentation in intracoronary ultrasound imaging–a statistical approach. TMI 23(5), 554–566 (2004)
Sonka, M., Zhang, X., Siebes, M., Bissing, M.S., Dejong, S.C., Collins, S.M., McKay, C.R.: Segmentation of intravascular ultrasound images: a knowledge-based approach. TMI 14(4), 719–732 (1995)
Unal, G., Bucher, S., Carlier, S., Slabaugh, G., Fang, T., Tanaka, K.: Shape-driven segmentation of the arterial wall in intravascular ultrasound images. TITB 12(3), 335–347 (2008)
Rotger, D., Radeva, P., Fernández-Nofrerías, E., Mauri, J.: Blood detection in ivus images for 3d volume of lumen changes measurement due to different drugs administration. In: CAIP, pp. 285–292 (2007)
Lafferty, J., Mccallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th ICML, pp. 282–289. Morgan Kaufmann, San Francisco (2001)
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. JAIR 2, 263–286 (1995)
Macqueen, J.B.: Some methods of classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Bovik, A.C., Clark, M., Geisler, W.S.: Multichannel texture analysis using localized spatial filters. TPAMI 12(1), 55–73 (1990)
Ojala, T., Pietikäien, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPAMI 24(7), 971–987 (2002)
Demi, M., Bianchini, E., Faita, F., Gemignani, V.: Contour tracking on ultrasound sequences of vascular images. PRIA 18(4), 606–612 (2008)
Vishwanathan, S.V.N., Schraudolph, N.N., Schmidt, M.W., Murphy, K.P.: Accelerated training of conditional random fields with stochastic gradient methods. In: ICML 2006, pp. 969–976. ACM, New York (2006)
Schapire, R.E.: The boosting approach to machine learning: An overview (2002)
Kumar, S., Hebert, M.: Discriminative fields for modeling spatial dependencies in natural images. In: Advances in Neural Information Processing Systems (2003)
Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding belief propagation and its generalizations, 239–269 (January 2002)
Escalera, S., Pujol, O., Radeva, P.: On the decoding process in ternary error-correcting output codes. TPAMI 99(1) (2009)
Pujol, O.: A semi-Supervised Statistical Framework and Generative Snakes for IVUS Analysis. PhD thesis, Autonomous University of Barcelona (2004)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: COLT, pp. 144–152 (1992)
Rifkin, R., Klautau, A.: In defense of one-vs-all classification. JMLR 5, 101–141 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ciompi, F., Pujol, O., Fernández-Nofrerías, E., Mauri, J., Radeva, P. (2009). ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04271-3_105
Download citation
DOI: https://doi.org/10.1007/978-3-642-04271-3_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04270-6
Online ISBN: 978-3-642-04271-3
eBook Packages: Computer ScienceComputer Science (R0)