A segmentation algorithm for noisy images: Design and evaluation

Abstract This paper presents a segmentation algorithm for gray-level images and addresses issues related to its performance on noisy images. It formulates an image segmentation problem as a partition of an image into (arbitrarily-shaped) connected regions to minimize the sum of gray-level variations over all partitioned regions, under the constraints that (1) each partitioned region has at least a specified number of pixels, and (2) two adjacent regions have significantly different “average” gray-levels. To overcome the computational difficulty of directly solving this problem, a minimum spanning tree representation of a gray-level image has been developed. With this tree representation, an image segmentation problem is effectively reduced to a tree partitioning problem, which can be solved efficiently. To evaluate the algorithm, we have studied how noise affects the performance of the algorithm. Three types of noise, transmission noise, Gaussian additive noise, and multiple sources of lightings, are considered, and their effects on the algorithm are studied.

[1]  H.M. Wechsler,et al.  Digital image processing, 2nd ed. , 1981, Proceedings of the IEEE.

[2]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[3]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[4]  Haluk Derin,et al.  Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  P. Bickel,et al.  Mathematical Statistics: Basic Ideas and Selected Topics , 1977 .

[6]  Ying Xu,et al.  2D image segmentation using minimum spanning trees , 1997, Image Vis. Comput..

[7]  Alfred V. Aho,et al.  The Design and Analysis of Computer Algorithms , 1974 .

[8]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[9]  Jan J. Gerbrands,et al.  Three-dimensional image segmentation using a split, merge and group approach , 1991, Pattern Recognit. Lett..

[10]  Peter E. Hart,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[11]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[12]  Mohan M. Trivedi,et al.  Low-Level Segmentation of Aerial Images with Fuzzy Clustering , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Yu Jin Zhang,et al.  Evaluation and comparison of different segmentation algorithms , 1997, Pattern Recognit. Lett..

[14]  Rama Chellappa,et al.  Stochastic and deterministic networks for texture segmentation , 1990, IEEE Trans. Acoust. Speech Signal Process..

[15]  Edward C. Uberbacher,et al.  Image exploitation using multisensor/neural network systems , 1995, Other Conferences.

[16]  Steven W. Zucker,et al.  Region growing: Childhood and adolescence* , 1976 .

[17]  E. Uberbacher,et al.  2-D image segmentation using minimum spanning trees , 1995 .

[18]  F. R. Hansen,et al.  Image segmentation using simple markov field models , 1982, Computer Graphics and Image Processing.