Abstract
Focus of research in Active contour models (ACM) area is mainly on development of various energy functions based on physical intuition. In this work, instead of designing a new energy function, we generate a multitude of contour candidates using various values of ACM parameters, assess their quality, and select the most suitable one for an object at hand. A random forest is trained to make contour quality assessments. We demonstrate experimentally superiority of the developed technique over three known algorithms in the P. minimum cells detection task solved via segmentation of phytoplankton images.
This is a preview of subscription content, log in via an institution to check access.
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
Kass, M., Witkin, A.P., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)
Chan, T., Vese, L.: Active contours without edges. IEEE Transactions Image Processing 10(2), 266–277 (2001)
Bernard, O., Friboulet, D., Thevenaz, P., Unser, M.: Variational B-spline level-set: A linear filtering approach for fast deformable model evolution. IEEE Transactions on Image Processing 18(6), 1179–1191 (2009)
Shang, Y., Yang, X., Zhu, L., Deklerck, R., Nyssen, E.: Region competition based active contour for medical object extraction. Computerized Medical Imaging and Graphics 32(2), 109–117 (2008)
Savelonas, M.A., Mylona, E.A., Maroulis, D.: Unsupervised 2D gel electrophoresis image segmentation based on active contours. Pattern Recognition 45(2), 720–731 (2012)
Veronese, E., Stramare, R., Campion, A., Raffeiner, B., Beltrame, V., Scagliori, E., Coran, A., Ciprian, L., Fiocco, U., Grisan, E.: Improved detection of synovial boundaries in ultrasound examination by using a cascade of active-contours. Medical Engineering & Physics (2012), doi.org/10.1016/j.medengphy.2012.04.014
Chakraborty, A., Staib, L., Duncan, J.: Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions Medical Imaging 15(6), 859–870 (1996)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)
Ronfard, R.: Region-based strategies for active contour models. International Journal of Computer Vision 13(2), 229–251 (1994)
Tao, W., Tai, X.C.: Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization. Image and Vision Computing 29, 499–508 (2011)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans Systems Man & Cybernetics 9(1), 62–66 (1979)
Verikas, A., Gelzinis, A., Bacauskiene, M.: Phase congruency-based detection of circular objects applied to analysis of phytoplankton images. Pattern Recognition 45(4), 1659–1670 (2012)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Shi, Y., Karl, W.C.: A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Transactions on Image Processing 17(5), 645–656 (2008)
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
Gelzinis, A., Verikas, A., Bacauskiene, M., Vaiciukynas, E. (2013). Learning Accurate Active Contours. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-41013-0_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41012-3
Online ISBN: 978-3-642-41013-0
eBook Packages: Computer ScienceComputer Science (R0)