Abstract
In this paper a left ventricle (LV) contour detection method is described. The method works from an approximate contour defined by anatomical landmarks extracted using Support Vector Machine (SVM) classifiers. The LV contour approximation is used as an initialization step for the deformable model algorithm. An optimization method based on a gradient descend algorithm is used to obtain the optimal contour that provides a minimum energy value. Both classifier and edge detection method performances have been validated. The error is determined as the difference between the shape estimated by the algorithm and the shape traced by an expert.
Keywords
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Sui, L., Haralick, R., Sheehan, F.: A knowledge–based boundary delineation system for contrast ventriculograms. IEEE Trans. Inform. Technol. Biomed. 5(2), 116–132 (2001)
Pratt, W.: Digital Image Processing. John Wiley - Sons, USA (1978)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: CVPR 1997, San Juan, Puerto Rico, pp. 130–136 (1997)
Smola, A.J.: Learning with Kernels. PhD thesis, Technische Universit
Burges, C.: A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining 2(2), 121–167 (1998)
Osuna, E., Freund, R., Girosi, F.: Support vector machines: Training and applications. Technical report, Artificial Intelligence Laboratory, Massachusetts Institute of Technology (1997)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contours models. Int. J. Comput. Vis. 1, 321–331 (1987)
Marr, D., Hildreth, E.: Theory of the edge detection. Proccedings of the Royal Society of London 207, 187–217 (1980)
Vera, M., Bravo, A.: Left ventricle image landmarks extraction using support vector machines. In: Proceedings of 2nd International Conference on Computer Vision Theory and Applications, Barcelona, Spain, pp. 339–343 (2007)
Barsky, B.A.: Computer Graphics and Geometric Modeling Using Beta–Splines. Springer, USA (1988)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intell. PAMI-8, 679–698 (1986)
Gonzalez, R., Woods, R.: Digital Image Processing. Addison–Wesley Publishing Company, New-Jersey (1992)
Haralick, R., Shapiro, L.: Computer and Robot Vision, vol. I. Addison-Wesley Publishing Company, USA (1992)
Hanbury, A., Serra, J.: Morphological operators on the unit circle. IEEE Trans. Image Processing 10(12), 1842–1850 (2001)
Ando, S.: Consistent gradient operators. IEEE Trans. Pattern Anal. Machine Intell. 22(3), 252–264 (2000)
Bravo, A., Medina, R., Diaz, J.A.: A clustering based approach for automatic image segmentation: An application to biplane ventriculograms. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 316–325. Springer, Heidelberg (2006)
Serra, J.: Image Analysis and Mathematical Morphology. A Press, London (1982)
Suykens, J., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)
Suzuki, K., Horiba, I., Sugie, N., Nanki, M.: Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Trans. Med. Imag. 23(3), 330–339 (2004)
Oost, E., Koning, G., Sonka, M., Oemrawsingh, P.V., Reiber, J.H.C., Lelieveldt, B.P.F.: Automated contour detection in x–ray left ventricular angiograms using multiview active appearance models and dynamic programming. IEEE Trans. Med. Imag. 25(9), 1158–1171 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bravo, A., Vera, M., Medina, R. (2007). Edge Detection in Ventriculograms Using Support Vector Machine Classifiers and Deformable Models. In: Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76725-1_82
Download citation
DOI: https://doi.org/10.1007/978-3-540-76725-1_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76724-4
Online ISBN: 978-3-540-76725-1
eBook Packages: Computer ScienceComputer Science (R0)