Two-dimensional minimum local cross-entropy thresholding based on co-occurrence matrix

Authors:
Fangyan Nie;Chao Gao;Yongcai Guo;Min Gan
Affiliations:
Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry, Chongqing University, Chongqing 400030, PR China and College of Computer Science and Technology, Hunan University ...;Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry, Chongqing University, Chongqing 400030, PR China;Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry, Chongqing University, Chongqing 400030, PR China;School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia
Venue:
Computers and Electrical Engineering
Year:
2011

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Abstract

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.