中文

Journal of Applied Sciences ›› 2011, Vol. 29 ›› Issue (5): 487-494.doi: 10.3969/j.issn.0255-8297.2011.05.008

• Signal and Information Processing • Previous Articles     Next Articles

Fast Iterative Thresholding Algorithm Based on Improved Two-Dimensional Minimum Cross Entropy

WU Yi-quan1;2, FAN Jun1, ZHOU Huai-chun2   

  1. 1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics,
    Nanjing 210016, China
    2. State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology,
    Wuhan 430074, China
  • Received:2011-03-28 Revised:2011-05-27 Online:2011-09-28 Published:2011-09-30

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Abstract:

The existing two-dimensional cross entropy thresholding method based on gray level-average gray level histogram does not produce accurate enough results. In addition, it needs to search the entire solution space to obtain the best threshold. In this paper, a fast iterative algorithm based on gray level-gradient cooccurrence matrix is proposed for selecting two-dimensional minimum cross-entropy threshold. The algorithmic formulas are derived. Experiments are carried out on typical test images. Comparisons of segmentation results and execution speed are made between the proposed method and the method based on gray level-average gray level 2D histogram. The results show that the proposed algorithm provides better segmentation. Only a small part of the solution space needs to be searched to find the best threshold. The running time reduces to about 5% of the method based on gray level-average gray level 2D histogram.

Key words:  image processing, thresholding, gray level-gradient co-occurrence matrix, minimum cross entropy, fast iterative algorithm

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