This Article 
 Bibliographic References 
 Add to: 
Segmenting Images Corrupted by Correlated Noise
May 1998 (vol. 20 no. 5)
pp. 481-492

Abstract—Image segmentation is fundamental to many image analysis problems. It aims to partition a digital image into a set of nonoverlapping homogeneous regions. The main contribution of this paper is the development of a new segmentation procedure which is designed to segment images corrupted by correlated noise. This new segmentation procedure is based on Rissanen's minimum description length (MDL) principle and consists of two components: i) an MDL-based criterion in which the "best" segmentation is defined as its minimizer and ii) a merging algorithm which attempts to locate this minimizer. The performance of this procedure is illustrated via a simulation study, with promising results.

[1] R. Adams and L. Bischof, “Seeded Region Growing,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641-647, June 1994.
[2] A.J. Baddeley, "Errors in Binary Images and an Lp Version of the Hausdorff Metric," Nieuw Archief voor Wiskunde, vol. 10, pp. 157-183, 1992.
[3] J.-M. Beaulieu and M. Goldberg, "Hierarchy in Picture Segmentation: A Stepwise Optimization Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, pp. 150-163, 1989.
[4] P.J. Besl and R.C. Jain,“Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 2, pp. 167-191, Mar. 1988.
[5] S. Bose and F. O'Sullivan, "A Region Based Image Segmentation Method for Multi-Channel Data," J. Am. Statistical Assoc., vol. 92, pp. 92-106, 1997.
[6] M.A. Cameron, "An Automatic Non-Parametric Spectrum Estimator," J. Time Series Analysis, vol. 8, pp. 379-387, 1987.
[7] Y.L. Chang and X.B. Li, “Adaptive Image Region-Growing,” IEEE Trans. Image Processing, vol. 3, pp. 868-87, 1994.
[8] R. Chellappa and R.L. Kashyap, “Texture Synthesis Using 2-D Noncausal Autoregressive Models,” IEEE Trans. Acoustics, Speech, Signal Processing, vol. 33, pp. 194-203, 1985.
[9] S.-Y. Chen, W.-C. Lin, and C.-T. Chen, "Split-and-Merge Image Segmentation Based on Localized Feature Analysis and Statistical Tests," CVGIP: Graphical Models and Image Processing, vol. 53, pp. 457-475, 1991.
[10] C.A. Glasbey and G.W. Horgan, Image Analysis for the Biological Sciences. John Wiley&Sons Ltd, 1995.
[11] R.M. Haralick and L.G. Shapiro, Computer and Robot Vision. Addison-Wesley Publishing Company, 1992.
[12] J.D. Hart, "Kernel Regression Estimation With Time Series Errors," J. Royal Statistical Soc. Series B, vol. 53, pp. 173-187, 1991.
[13] A.K. Jain, Fundamentals of Digital Image Processing.Englewood Cliffs, NJ: Prentice-Hall, 1989.
[14] V.E. Johnson, "A Model for Segmentation and Analysis of Noisy Images," J. Am. Statistical Assoc., vol. 89, pp. 230-241, 1994.
[15] T. Kanungo, B. Dom, W. Niblack, D. Steele, and J. Sheinvald, "MDL-Based Multi-Band Image Segmentation Using a Fast Region Merging Scheme," Technical Report RJ 9960 (87919), IBM Research Division, 1995.
[16] R.L. Kashyap, "Characterization and Estimation of Two-Dimensional ARMA Models," IEEE Trans. Information Theory, vol. 30, pp. 736-745, 1984.
[17] S.M. Lavalle and S.A. Hutchinson, “A Bayesian Segmentation Methodology for Parametric Image Models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, pp. 211-217, 1995.
[18] Y.G. Leclerc, "Constructing Simple Stable Descriptions for Image Partitioning," Int'l J. Computer Vision, vol. 3, pp. 73-102, 1989.
[19] T.C.M. Lee, "A Minimum Description Length Based Image Segmentation Procedure, and Its Comparison With a Cross-Validation Based Segmentation Procedure," 1997. Unpublished manuscript.
[20] T.C.M. Lee, "Some Models and Methods in Image Segmentation," PhD thesis, Macquarie Univ., Sydney, Australia, 1997.
[21] J.W. Modestino, R.W. Fries, and A.L. Vickers, "Texture Discrimination Based Upon an Assumed Stochastic Texture Model," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 3, pp. 557-580, 1981.
[22] J. Rissanen, Stochastic Complexity in Statistical Inquiry. World Scientific, Singapore, 1989.
[23] V. Solo, "Modeling of Two-Dimensional Random Fields by Parametric Cepstrum," IEEE Trans. Information Theory, vol. 32, pp. 743-750, 1986.
[24] G. Taubin,“Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 11, pp. 1115-1137, Nov. 1991.
[25] A.L. Vickers and J.W. Modestino, "A Maximum Likelihood Approach to Texture Classification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 4, pp. 61-68, 1982.
[26] J. Zhang and J.W. Modestino,“A model-fitting approach to cluster validation with applications to stochastic model-based image segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 1009-1017, Oct. 1990.
[27] S.C. Zhu and A. Yuille, “Region Competition: Unifying Snakes, Region Growing and Bayes/MDL for Multiband Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 884-900, 1996.

Index Terms:
Correlated noise, image segmentation, merging algorithm, minimum description length.
Thomas C.M. Lee, "Segmenting Images Corrupted by Correlated Noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 5, pp. 481-492, May 1998, doi:10.1109/34.682178
Usage of this product signifies your acceptance of the Terms of Use.