Abstract
We propose new techniques for unsupervised segmentation of multi-modal grayscale images such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of gray levels. We follow most conventional approaches such that initial images and desired maps of regions are described by a joint Markov–Gibbs random field (MGRF) model of independent image signals and interdependent region labels. But our focus is on more accurate model identification. To better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. Initial segmentation based on the LCG-models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage is discussed. Experiments with medical images show that the proposed segmentation is more accurate than other known alternatives.
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
Kaufhold, J., Hoogs, A.: Learning to segment images using region-based perceptual features. In: Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2004), vol. 1, pp. 954–961 (2004)
Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proc. 9th IEEE Int. Conf. on Computer Vision (ICCV 2003), Nice, France, October 14–17, vol. 1, pp. 10–17. IEEE CS Press, Los Alamitos (2003)
Tu, Z., Chen, X., Yuille, A.L., Zhu, S.C.: Image parsing: Unifying segmentation, detection, and recognition. In: Proc. 9th IEEE Int. Conf. on Computer Vision (ICCV 2003), Nice, France, October 14–17, vol. 1, pp. 18–25. IEEE CS Press, Los Alamitos (2003)
Yu, S.X.: Segmentation using multiscale cues. In: Proc IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2004), vol. 1, pp. 247–254 (2004)
Singh, M., Ahuja, N.: Regression based bandwidth selection for segmentation using Parzen windows. In: Proc. 9th IEEE Int. Conf. on Computer Vision (ICCV 2003), Nice, France, October 14–17, 2003, vol. 1, pp. 2–9. IEEE CS Press, Los Alamitos (2003)
Grzeszczuk, R.P., Levin, D.N.: Brownian strings: Segmenting images with stochastically deformable contours. IEEE Trans. Pattern Anal. Machine Intell. 19, 1100–1114 (1997)
Huang, X., Metaxas, D., Chen, T.: MetaMorphs: Deformable shape and texture models. In: Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2004), vol. 1, pp. 496–503 (2004)
Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Pattern Anal. Machine Intell. 7(3), 359–369 (1998)
Yuen, P.C., Feng, G.C., Zhou, J.P.: A contour detection method: Initialization and contour model. Pattern Recognition Letters 20, 141–148 (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Schlesinger, M.I., Hlavac, V.: Ten Lectures on Statistical and Structural Pattern Recognition. Kluwer, Dordrecht (2002)
Bouman, C.A., Shapiro, M.: A multiscale random field model for Bayesian image segmentation. IEEE Trans. Image Processing 3(2), 162–177 (1994)
Farag, A.A., El-Baz, A., Gimelfarb, G.: Density Estimation Using Modified Expectation Maximization for a linear combination of Gaussians. In: Proc. of IEEE International Conference on Image Processing (ICIP 2004), Singapore, October 24–27, vol. I, pp. 194–197 (2004)
Dubes, R.C., Jain, A.K.: Random field models in image analysis. J. Applied Statistics 16(2), 131–164 (1989)
Picard, R.W., Elfadel, I.M.: Structure of aura and cooccurrence matrices for the Gibbs texture model. J. Math. Imaging and Vision 2(1), 5–25 (1992)
Dryden, L., Scarr, M.R., Taylor, C.C.: Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods. J. Royal Statistical Society 52(1), 31–50 (2003)
Lamperti, J.W.: Probability. J. Wiley & Sons, New York (1996)
Hu, S., Hoffman, E.A.: Automatic lung segmentation for accurate quantization of volumetric X-Ray CT images. IEEE Trans. Medical Imaging 20(6), 490–498 (2001)
Bouman, C., Liu, B.: Multiple resolution segmentation of textured images. IEEE Trans. Pattern Analysis Machine Intell. 13(2), 99–113 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
El-Baz, A., Farag, A., Ali, A., Gimel’farb, G., Casanova, M. (2006). A Framework for Unsupervised Segmentation of Multi-modal Medical Images. In: Beichel, R.R., Sonka, M. (eds) Computer Vision Approaches to Medical Image Analysis. CVAMIA 2006. Lecture Notes in Computer Science, vol 4241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11889762_11
Download citation
DOI: https://doi.org/10.1007/11889762_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46257-6
Online ISBN: 978-3-540-46258-3
eBook Packages: Computer ScienceComputer Science (R0)