Journal
COMPUTERS & ELECTRICAL ENGINEERING
Volume 37, Issue 5, Pages 757-767Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2011.06.006
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Funding
- Chinese Ministry of Education [108174]
- Ph.D. Programs Foundation of Ministry of Education of China [200806110016]
- Chongqing Municipal Natural Science Foundation of China [CSTC2008BB3169]
- Foundation of 11th Five-year Plan for Key Construction Academic Subject (Optics) of Hunan Province, China
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This paper introduces a novel image segmentation method that performs histogram thresholding on an image with consideration to spatial information. The spatial information is the joint gray level values of the pixel to be segmented and its neighboring pixels that are based on the gray level co-occurrence matrix (GLCM). The new method was obtained by extending the one-dimensional (1D) cross-entropy thresholding method to a two-dimensional (2D) one in the GLCM. Firstly, the 2D local cross-entropy is defined at the local quadrants of the GLCM. Then, the 2D local cross-entropy is used to perform the optimal threshold selection by minimizing. Results from segmenting the real-world images demonstrate that the new method is capable of achieving better results when compared with 1D cross-entropy and other classical GLCM based thresholding methods. (C) 2011 Elsevier Ltd. All rights reserved.
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